{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,28]],"date-time":"2026-07-28T14:44:45Z","timestamp":1785249885673,"version":"3.55.0"},"reference-count":186,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2026,3,26]],"date-time":"2026-03-26T00:00:00Z","timestamp":1774483200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2026,5,13]],"date-time":"2026-05-13T00:00:00Z","timestamp":1778630400000},"content-version":"vor","delay-in-days":48,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"funder":[{"DOI":"10.13039\/100019725","name":"Deanship of Scientific Research, Prince Sattam bin Abdulaziz University","doi-asserted-by":"crossref","award":["PSAU\/2025\/RV\/16"],"award-info":[{"award-number":["PSAU\/2025\/RV\/16"]}],"id":[{"id":"10.13039\/100019725","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int J Comput Intell Syst"],"DOI":"10.1007\/s44196-026-01283-2","type":"journal-article","created":{"date-parts":[[2026,3,26]],"date-time":"2026-03-26T08:58:31Z","timestamp":1774515511000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Systematic Review for Detecting &amp; Diagnosing Brain Tumor Using Deep Learning: Current Trends and Future Research Directions"],"prefix":"10.1007","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7672-1187","authenticated-orcid":false,"given":"Usman","family":"Tariq","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5648-388X","authenticated-orcid":false,"given":"Irfan","family":"Ahmed","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2174-3383","authenticated-orcid":false,"given":"Kamran","family":"Shaukat","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8753-7884","authenticated-orcid":false,"given":"Mansoor","family":"Ihsan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,3,26]]},"reference":[{"issue":"6","key":"1283_CR1","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1002\/pbc.30980","volume":"71","author":"BD Padhye","year":"2024","unstructured":"Padhye, B.D., et al.: Proteomic insights into paediatric cancer: unravelling molecular signatures and therapeutic opportunities. Pediatr. Blood Cancer 71(6), 1\u201312 (2024). https:\/\/doi.org\/10.1002\/pbc.30980","journal-title":"Pediatr. Blood Cancer"},{"key":"1283_CR2","doi-asserted-by":"publisher","first-page":"1","DOI":"10.3389\/fphar.2023.1250699","volume":"14","author":"D Bartusik-Aebisher","year":"2023","unstructured":"Bartusik-Aebisher, D., Serafin, I., Dynarowicz, K., Aebisher, D.: Photodynamic therapy and associated targeting methods for treatment of brain cancer. Front. Pharmacol. 14, 1\u201316 (2023). https:\/\/doi.org\/10.3389\/fphar.2023.1250699","journal-title":"Front. Pharmacol."},{"issue":"1","key":"1283_CR3","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s40644-022-00455-5","volume":"22","author":"S Vagvala","year":"2022","unstructured":"Vagvala, S., Guenette, J.P., Jaimes, C., Huang, R.Y.: Imaging diagnosis and treatment selection for brain tumors in the era of molecular therapeutics. Cancer Imaging 22(1), 1\u201314 (2022). https:\/\/doi.org\/10.1186\/s40644-022-00455-5","journal-title":"Cancer Imaging"},{"issue":"3","key":"1283_CR4","doi-asserted-by":"publisher","first-page":"479","DOI":"10.1007\/s11060-020-03667-6","volume":"151","author":"CM Rogers","year":"2021","unstructured":"Rogers, C.M., Jones, P.S., Weinberg, J.S.: Intraoperative MRI for brain tumors. J. Neurooncol 151(3), 479\u2013490 (2021). https:\/\/doi.org\/10.1007\/s11060-020-03667-6","journal-title":"J. Neurooncol"},{"issue":"1","key":"1283_CR5","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41598-022-19465-1","volume":"12","author":"U Haq","year":"2022","unstructured":"Haq, U., Li, J.P., Khan, S., Alshara, M.A., Alotaibi, R.M., Mawuli, C.: DACBT: deep learning approach for classification of brain tumors using MRI data in IoT healthcare environment. Sci. Rep. 12(1), 1\u201314 (2022). https:\/\/doi.org\/10.1038\/s41598-022-19465-1","journal-title":"Sci. Rep."},{"key":"1283_CR6","doi-asserted-by":"publisher","first-page":"3611","DOI":"10.1007\/s11042-024-18942-1","volume":"84","author":"T Tassew","year":"2024","unstructured":"Tassew, T., Ashamo, B.A., Nie, X.: Multimodal MRI brain tumor segmentation using 3D attention UNet with dense encoder blocks and residual decoder blocks. Multim. Tools Appl. 84, 3611\u20133633 (2024). https:\/\/doi.org\/10.1007\/s11042-024-18942-1","journal-title":"Multim. Tools Appl."},{"issue":"1","key":"1283_CR7","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41598-024-72342-x","volume":"14","author":"L Ali","year":"2024","unstructured":"Ali, L., Alnajjar, F., Swavaf, M., Elharrouss, O., Abd-Alrazaq, A., Damseh, R.: Evaluating segment anything model (SAM) on MRI scans of brain tumors. Sci. Rep. 14(1), 1\u201316 (2024). https:\/\/doi.org\/10.1038\/s41598-024-72342-x","journal-title":"Sci. Rep."},{"issue":"3","key":"1283_CR8","doi-asserted-by":"publisher","first-page":"616","DOI":"10.1016\/j.bbe.2023.08.003","volume":"43","author":"K Sahoo","year":"2023","unstructured":"Sahoo, K., Parida, P., Muralibabu, K., Dash, S.: Efficient simultaneous segmentation and classification of brain tumors from MRI scans using deep learning. J. Appl. Biomed. 43(3), 616\u2013633 (2023). https:\/\/doi.org\/10.1016\/j.bbe.2023.08.003","journal-title":"J. Appl. Biomed."},{"issue":"25","key":"1283_CR9","doi-asserted-by":"publisher","first-page":"30385","DOI":"10.1007\/s11042-024-20398-2","volume":"84","author":"J Shreeharsha","year":"2024","unstructured":"Shreeharsha, J.: Detection of brain tumor using hybridized 3D U-Net model on MRI images. Multim. Tools Appl. 84(25), 30385\u201330413 (2024). https:\/\/doi.org\/10.1007\/s11042-024-20398-2","journal-title":"Multim. Tools Appl."},{"key":"1283_CR10","doi-asserted-by":"publisher","first-page":"36111","DOI":"10.1007\/s11042-021-11504-9","volume":"80","author":"HM Rai","year":"2021","unstructured":"Rai, H.M., Chatterjee, K.: 2D MRI image analysis and brain tumor detection using deep learning CNN model LeU-Net. Multim. Tools Appl. 80, 36111\u201336141 (2021). https:\/\/doi.org\/10.1007\/s11042-021-11504-9","journal-title":"Multim. Tools Appl."},{"issue":"2","key":"1283_CR11","doi-asserted-by":"publisher","DOI":"10.3390\/brainsci13020348","volume":"13","author":"TN Papadomanolakis","year":"2023","unstructured":"Papadomanolakis, T.N., et al.: Tumor diagnosis against other brain diseases using T2 MRI brain images and CNN binary classifier and DWT. Brain Sci. 13(2), 348 (2023). https:\/\/doi.org\/10.3390\/brainsci13020348","journal-title":"Brain Sci."},{"issue":"1","key":"1283_CR12","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41598-023-50505-6","volume":"13","author":"BB Vimala","year":"2023","unstructured":"Vimala, B.B., Srinivasan, S., Mathivanan, S.K., Mahalakshmi, N., Jayagopal, P., Dalu, G.T.: Detection and classification of brain tumor using hybrid deep learning models. Sci. Rep. 13(1), 1\u201317 (2023). https:\/\/doi.org\/10.1038\/s41598-023-50505-6","journal-title":"Sci. Rep."},{"key":"1283_CR13","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1155\/2022\/2092985","volume":"2022","author":"T Han-Trong","year":"2022","unstructured":"Han-Trong, T., Van, H.N., Thanh, H.N.T., Anh, V.T., Tuan, D.N., Dang, L.V.: An efficient method for diagnosing brain tumors based on MRI images using deep convolutional neural networks. Appl. Comput. Intell. Soft. Comput. 2022, 1\u201318 (2022). https:\/\/doi.org\/10.1155\/2022\/2092985","journal-title":"Appl. Comput. Intell. Soft. Comput."},{"issue":"9","key":"1283_CR14","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0307825","volume":"19","author":"N Aziz","year":"2024","unstructured":"Aziz, N., Minallah, N., Frnda, J., Sher, M., Zeeshan, M., Durrani, A.H.: Precision meets generalization: enhancing brain tumor classification via pretrained DenseNet with global average pooling and hyperparameter tuning. PLoS ONE 19(9), e0307825 (2024). https:\/\/doi.org\/10.1371\/journal.pone.0307825","journal-title":"PLoS ONE"},{"issue":"1","key":"1283_CR15","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41598-025-04591-3","volume":"15","author":"J Aiya","year":"2025","unstructured":"Aiya, J., et al.: Optimized deep learning for brain tumor detection: a hybrid approach with attention mechanisms and clinical explainability. Sci. Rep. 15(1), 1\u201322 (2025). https:\/\/doi.org\/10.1038\/s41598-025-04591-3","journal-title":"Sci. Rep."},{"issue":"10","key":"1283_CR16","doi-asserted-by":"publisher","DOI":"10.3390\/info15100653","volume":"15","author":"KAKW Dewage","year":"2024","unstructured":"Dewage, K.A.K.W., Hasan, R., Rehman, B., Mahmood, S.: Enhancing brain tumor detection through custom convolutional neural networks and interpretability-driven analysis. Information 15(10), 653 (2024). https:\/\/doi.org\/10.3390\/info15100653","journal-title":"Information"},{"key":"1283_CR17","doi-asserted-by":"publisher","first-page":"103077","DOI":"10.1016\/j.bspc.2021.103077","volume":"71","author":"D Maji","year":"2021","unstructured":"Maji, D., Sigedar, P., Singh, M.: Attention Res-UNet with guided decoder for semantic segmentation of brain tumors. Biomed. Signal Process. Control 71, 103077 (2021). https:\/\/doi.org\/10.1016\/j.bspc.2021.103077","journal-title":"Biomed. Signal Process. Control"},{"key":"1283_CR18","doi-asserted-by":"publisher","DOI":"10.1016\/j.rineng.2024.102994","author":"W Zafar","year":"2024","unstructured":"Zafar, W., et al.: Enhanced TumorNet: leveraging YOLOV8s and U-Net for superior brain tumor detection and segmentation utilizing MRI scans. Results Eng. (2024). https:\/\/doi.org\/10.1016\/j.rineng.2024.102994","journal-title":"Results Eng."},{"issue":"8","key":"1283_CR19","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/jbhi.2024.3467343","volume":"29","author":"Y Ge","year":"2024","unstructured":"Ge, Y., Xu, L., Wang, X., Que, Y., Piran, M.J.: A novel framework for multimodal brain tumor detection with scarce labels. IEEE J. Biomed. Health Inform. 29(8), 1\u201314 (2024). https:\/\/doi.org\/10.1109\/jbhi.2024.3467343","journal-title":"IEEE J. Biomed. Health Inform."},{"issue":"19","key":"1283_CR20","doi-asserted-by":"publisher","first-page":"14432","DOI":"10.3390\/ijms241914432","volume":"24","author":"Ospanov","year":"2023","unstructured":"Ospanov, et al.: Optical Differentiation of Brain Tumors Based on Raman Spectroscopy and Cluster Analysis Methods. Int. J. Mol. Sci. 24(19), 14432 (2023). https:\/\/doi.org\/10.3390\/ijms241914432","journal-title":"Int. J. Mol. Sci."},{"issue":"1","key":"1283_CR21","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41598-025-09311-5","volume":"15","author":"S Panigrahi","year":"2025","unstructured":"Panigrahi, S., Adhikary, D.R.D., Pattanayak, B.K.: Hybrid transfer learning and self-attention framework for robust MRI-based brain tumor classification. Sci. Rep. 15(1), 1\u201330 (2025). https:\/\/doi.org\/10.1038\/s41598-025-09311-5","journal-title":"Sci. Rep."},{"key":"1283_CR22","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2025.108285","volume":"110","author":"Z Zhou","year":"2025","unstructured":"Zhou, Z., et al.: Comprehensive exploiting local and global features for brain tumor segmentation: a gated dual-branch hybrid attention mechanism. Biomed. Signal Process. Control 110, 108285 (2025). https:\/\/doi.org\/10.1016\/j.bspc.2025.108285","journal-title":"Biomed. Signal Process. Control"},{"issue":"5","key":"1283_CR23","doi-asserted-by":"publisher","DOI":"10.3390\/app14052210","volume":"14","author":"S Natha","year":"2024","unstructured":"Natha, S., Laila, U., Gashim, I.A., Mahboob, K., Saeed, M.N., Noaman, K.M.: Automated brain tumor identification in biomedical radiology images: a multi-model ensemble deep learning approach. Appl. Sci. (Basel) 14(5), 2210 (2024). https:\/\/doi.org\/10.3390\/app14052210","journal-title":"Appl. Sci. (Basel)"},{"issue":"19","key":"1283_CR24","doi-asserted-by":"publisher","DOI":"10.3390\/s22197575","volume":"22","author":"N Ullah","year":"2022","unstructured":"Ullah, N., Khan, M.S., Khan, J.A., Choi, A., Anwar, M.S.: A robust end-to-end deep learning-based approach for effective and reliable BTD using MR images. Sensors (Basel) 22(19), 7575 (2022). https:\/\/doi.org\/10.3390\/s22197575","journal-title":"Sensors (Basel)"},{"key":"1283_CR25","doi-asserted-by":"publisher","first-page":"1","DOI":"10.3389\/fnins.2022.837646","volume":"16","author":"X Liu","year":"2022","unstructured":"Liu, X., et al.: Unsupervised black-box model domain adaptation for brain tumor segmentation. Front. Neurosci. 16, 1\u201311 (2022). https:\/\/doi.org\/10.3389\/fnins.2022.837646","journal-title":"Front. Neurosci."},{"issue":"3","key":"1283_CR26","doi-asserted-by":"publisher","first-page":"1261","DOI":"10.1109\/jbhi.2023.3266614","volume":"28","author":"S Hossain","year":"2023","unstructured":"Hossain, S., Chakrabarty, A., Gadekallu, T.R., Alazab, M., Piran, M.J.: Vision transformers, ensemble model, and transfer learning leveraging explainable AI for brain tumor detection and classification. IEEE J. Biomed. Health Inform. 28(3), 1261\u20131272 (2023). https:\/\/doi.org\/10.1109\/jbhi.2023.3266614","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"1283_CR27","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2025.122046","volume":"708","author":"L Huang","year":"2025","unstructured":"Huang, L., et al.: PCG-CAM: enhanced class activation map using principal components of gradients and its applications in brain MRI. Inf. Sci. 708, 122046 (2025). https:\/\/doi.org\/10.1016\/j.ins.2025.122046","journal-title":"Inf. Sci."},{"key":"1283_CR28","doi-asserted-by":"publisher","first-page":"154172","DOI":"10.1109\/access.2025.3603272","volume":"13","author":"YH Chel","year":"2025","unstructured":"Chel, Y.H., Poh, L.L.: Brain tumor classification in MRI: insights from LIME and Grad-CAM explainable AI techniques. IEEE Access 13, 154172\u2013154202 (2025). https:\/\/doi.org\/10.1109\/access.2025.3603272","journal-title":"IEEE Access"},{"key":"1283_CR29","doi-asserted-by":"publisher","first-page":"1","DOI":"10.3389\/fncom.2024.1452457","volume":"18","author":"M Dibaji","year":"2024","unstructured":"Dibaji, M., Ospel, J., Souza, R., Bento, M.: Sex differences in brain MRI using deep learning toward fairer healthcare outcomes. Front. Comput. Neurosci. 18, 1\u201313 (2024). https:\/\/doi.org\/10.3389\/fncom.2024.1452457","journal-title":"Front. Comput. Neurosci."},{"issue":"1","key":"1283_CR30","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s13014-023-02246-z","volume":"18","author":"J-Y Wang","year":"2023","unstructured":"Wang, J.-Y., et al.: Stratified assessment of an FDA-cleared deep learning algorithm for automated detection and contouring of metastatic brain tumors in stereotactic radiosurgery. Radiat. Oncol. 18(1), 1\u20137 (2023). https:\/\/doi.org\/10.1186\/s13014-023-02246-z","journal-title":"Radiat. Oncol."},{"key":"1283_CR31","doi-asserted-by":"publisher","first-page":"140722","DOI":"10.1109\/access.2024.3456599","volume":"12","author":"K Lata","year":"2024","unstructured":"Lata, K., Singh, P., Saini, S., Cenkeramaddi, L.R.: Deep learning-based brain tumor detection in privacy-preserving smart health care systems. IEEE Access 12, 140722\u2013140733 (2024). https:\/\/doi.org\/10.1109\/access.2024.3456599","journal-title":"IEEE Access"},{"issue":"1","key":"1283_CR32","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41698-024-00575-0","volume":"8","author":"S Khalighi","year":"2024","unstructured":"Khalighi, S., Reddy, K., Midya, A., Pandav, K.B., Madabhushi, A., Abedalthagafi, M.: Artificial intelligence in neuro-oncology: advances and challenges in brain tumor diagnosis, prognosis, and precision treatment. NPJ Precis. Oncol. 8(1), 1\u201312 (2024). https:\/\/doi.org\/10.1038\/s41698-024-00575-0","journal-title":"NPJ Precis. Oncol."},{"issue":"20","key":"1283_CR33","doi-asserted-by":"publisher","DOI":"10.1016\/j.heliyon.2024.e38997","volume":"10","author":"MI Nazir","year":"2024","unstructured":"Nazir, M.I., Akter, A., Wadud, M.A.H., Uddin, M.A.: Utilizing customized CNN for brain tumor prediction with explainable AI. Heliyon 10(20), e38997 (2024). https:\/\/doi.org\/10.1016\/j.heliyon.2024.e38997","journal-title":"Heliyon"},{"issue":"16","key":"1283_CR34","doi-asserted-by":"publisher","DOI":"10.3390\/cancers14164052","volume":"14","author":"B Jena","year":"2022","unstructured":"Jena, B., et al.: Brain tumor characterization using radiogenomics in artificial intelligence framework. Cancers 14(16), 4052 (2022). https:\/\/doi.org\/10.3390\/cancers14164052","journal-title":"Cancers"},{"key":"1283_CR35","doi-asserted-by":"publisher","first-page":"1","DOI":"10.3389\/fonc.2023.1137803","volume":"13","author":"JJ Lucido","year":"2023","unstructured":"Lucido, J.J., et al.: Validation of clinical acceptability of deep-learning-based automated segmentation of organs-at-risk for head-and-neck radiotherapy treatment planning. Front. Oncol. 13, 1\u201315 (2023). https:\/\/doi.org\/10.3389\/fonc.2023.1137803","journal-title":"Front. Oncol."},{"issue":"4","key":"1283_CR36","doi-asserted-by":"publisher","first-page":"173","DOI":"10.1093\/ons\/opab135","volume":"21","author":"CR Wagner","year":"2021","unstructured":"Wagner, C.R., Phillips, T., Roux, S., Corrigan, J.P.: Future directions in robotic neurosurgery. Oper. Neurosurg. 21(4), 173\u2013180 (2021). https:\/\/doi.org\/10.1093\/ons\/opab135","journal-title":"Oper. Neurosurg."},{"key":"1283_CR37","unstructured":"L. Shamseer et al.: Protocols \u2014 PRISMA statement, PRISMA Statement 2015. https:\/\/www.prisma-statement.org\/protocols.(accessed 15 Sep. 2025)."},{"key":"1283_CR38","unstructured":"University of York PROSPERO 2025. https:\/\/www.crd.york.ac.uk\/prospero\/.(accessed Sep. 15, 2025)."},{"issue":"8","key":"1283_CR39","doi-asserted-by":"publisher","DOI":"10.7326\/0003-4819-155-8-201110180-00009","volume":"155","author":"PF Whiting","year":"2011","unstructured":"Whiting, P.F.: QUADAS-2: a revised tool for the quality assessment of diagnostic accuracy studies. Ann. Intern. Med. 155(8), 529 (2011). https:\/\/doi.org\/10.7326\/0003-4819-155-8-201110180-00009","journal-title":"Ann. Intern. Med."},{"key":"1283_CR40","unstructured":"University of Bristol QUADAS | Bristol Medical School: Population Health Sciences | University of Bristol Apr. 30, 2025. https:\/\/www.bristol.ac.uk\/population-health-sciences\/projects\/quadas\/ (accessed 15\u00a0 Sep 2025)."},{"key":"1283_CR41","doi-asserted-by":"publisher","first-page":"156","DOI":"10.1109\/rbme.2019.2946868","volume":"13","author":"M Ghaffari","year":"2019","unstructured":"Ghaffari, M., Sowmya, A., Oliver, R.: Automated brain tumor segmentation using multimodal Brain scans: A survey based on models submitted to the BRATS 2012\u20132018 challenges. IEEE Rev. Biomed. Eng. 13, 156\u2013168 (2019). https:\/\/doi.org\/10.1109\/rbme.2019.2946868","journal-title":"IEEE Rev. Biomed. Eng."},{"issue":"10","key":"1283_CR42","doi-asserted-by":"publisher","first-page":"1993","DOI":"10.1109\/tmi.2014.2377694","volume":"34","author":"BH Menze","year":"2014","unstructured":"Menze, B.H., et al.: The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS). IEEE Trans. Med. Imaging 34(10), 1993\u20132024 (2014). https:\/\/doi.org\/10.1109\/tmi.2014.2377694","journal-title":"IEEE Trans. Med. Imaging"},{"key":"1283_CR43","doi-asserted-by":"publisher","unstructured":"T. Henry et al.:Brain Tumor Segmentation with Self-ensembled, Deeply-Supervised 3D U-Net Neural Networks: A BraTS 2020 Challenge Solution in Lecture notes in computer science pp. 327\u2013339 (2021). https:\/\/doi.org\/10.1007\/978-3-030-72084-1_30","DOI":"10.1007\/978-3-030-72084-1_30"},{"issue":"3","key":"1283_CR44","doi-asserted-by":"publisher","first-page":"674","DOI":"10.1002\/ima.22407","volume":"30","author":"PL Chithra","year":"2020","unstructured":"Chithra, P.L., Dheepa, G.: Di\u2010phase midway convolution and deconvolution network for brain tumor segmentation in MRI images. Int. J. Imaging Syst. Technol. 30(3), 674\u2013686 (2020). https:\/\/doi.org\/10.1002\/ima.22407","journal-title":"Int. J. Imaging Syst. Technol."},{"key":"1283_CR45","doi-asserted-by":"publisher","first-page":"180134","DOI":"10.1109\/access.2019.2958370","volume":"7","author":"M Li","year":"2019","unstructured":"Li, M., Kuang, L., Xu, S., Sha, Z.: Brain tumor detection based on multimodal information fusion and convolutional neural network. IEEE Access 7, 180134\u2013180146 (2019). https:\/\/doi.org\/10.1109\/access.2019.2958370","journal-title":"IEEE Access"},{"key":"1283_CR46","doi-asserted-by":"publisher","first-page":"104296","DOI":"10.1016\/j.bspc.2022.104296","volume":"80","author":"Y Cao","year":"2022","unstructured":"Cao, Y., Zhou, W., Zang, M., An, D., Feng, Y., Yu, B.: MBANet: a 3D convolutional neural network with multi-branch attention for brain tumor segmentation from MRI images. Biomed. Signal Process. Control 80, 104296 (2022). https:\/\/doi.org\/10.1016\/j.bspc.2022.104296","journal-title":"Biomed. Signal Process. Control"},{"key":"1283_CR47","doi-asserted-by":"publisher","first-page":"118833","DOI":"10.1016\/j.eswa.2022.118833","volume":"213","author":"MG Allah","year":"2022","unstructured":"Allah, M.G., Sarhan, A.M., Elshennawy, N.M.: Edge U-Net: brain tumor segmentation using MRI based on deep U-Net model with boundary information. Expert Syst. Appl. 213, 118833 (2022). https:\/\/doi.org\/10.1016\/j.eswa.2022.118833","journal-title":"Expert Syst. Appl."},{"key":"1283_CR48","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1080\/01616412.2025.2517312","volume":"2025","author":"NE Varghese","year":"2025","unstructured":"Varghese, N.E., John, A., C, U.D.A., Pillai, M.J.: Transformer-augmented lightweight U-Net (UAAC-Net) for accurate MRI brain tumor segmentation. Neurol. Res. 2025, 1\u201316 (2025). https:\/\/doi.org\/10.1080\/01616412.2025.2517312","journal-title":"Neurol. Res."},{"key":"1283_CR49","doi-asserted-by":"publisher","first-page":"16","DOI":"10.1109\/tim.2024.3413130","volume":"73","author":"Y Liu","year":"2024","unstructured":"Liu, Y., Ma, Y., Zhu, Z., Cheng, J., Chen, X.: TransSea: hybrid CNN-Transformer with semantic awareness for 3D brain tumor segmentation. IEEE Trans. Instrum. Meas. 73, 16\u201331 (2024). https:\/\/doi.org\/10.1109\/tim.2024.3413130","journal-title":"IEEE Trans. Instrum. Meas."},{"issue":"7","key":"1283_CR50","doi-asserted-by":"publisher","DOI":"10.3390\/s22072726","volume":"22","author":"MM Zahoor","year":"2022","unstructured":"Zahoor, M.M., et al.: A new deep hybrid boosted and ensemble learning-based brain tumor analysis using MRI. Sensors 22(7), 2726 (2022). https:\/\/doi.org\/10.3390\/s22072726","journal-title":"Sensors"},{"issue":"6","key":"1283_CR51","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1111\/exsy.13226","volume":"40","author":"HS Ali","year":"2022","unstructured":"Ali, H.S., Ismail, A.I., El\u2010Rabaie, E.M., El\u2010Samie, F.E.A.: Deep residual architectures and ensemble learning for efficient brain tumour classification. Expert. Syst. 40(6), 1\u201316 (2022). https:\/\/doi.org\/10.1111\/exsy.13226","journal-title":"Expert. Syst."},{"issue":"11","key":"1283_CR52","doi-asserted-by":"publisher","first-page":"1357","DOI":"10.1093\/neuonc\/noz123","volume":"21","author":"QT Ostrom","year":"2019","unstructured":"Ostrom, Q.T., et al.: Risk factors for childhood and adult primary brain tumors. Neuro Oncol. 21(11), 1357\u20131375 (2019). https:\/\/doi.org\/10.1093\/neuonc\/noz123","journal-title":"Neuro Oncol."},{"issue":"10412","key":"1283_CR53","doi-asserted-by":"publisher","first-page":"1564","DOI":"10.1016\/s0140-6736(23)01054-1","volume":"402","author":"MJ Van Den Bent","year":"2023","unstructured":"Van Den Bent, M.J., et al.: Primary brain tumours in adults. The Lancet 402(10412), 1564\u20131579 (2023). https:\/\/doi.org\/10.1016\/s0140-6736(23)01054-1","journal-title":"The Lancet"},{"issue":"1","key":"1283_CR54","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s12967-019-2103-0","volume":"17","author":"H Zhu","year":"2019","unstructured":"Zhu, H., et al.: The clinical characteristics and molecular mechanism of pituitary adenoma associated with meningioma. J. Transl. Med. 17(1), 1\u20139 (2019). https:\/\/doi.org\/10.1186\/s12967-019-2103-0","journal-title":"J. Transl. Med."},{"key":"1283_CR55","doi-asserted-by":"publisher","DOI":"10.1016\/j.cclet.2025.110995","volume":"In Press","author":"D Yu","year":"2025","unstructured":"Yu, D., et al.: Knowledge structures and research hotspots of immunotherapy for brain metastasis, glioma, meningioma, and pituitary adenoma: a bibliometric and visualization review. Chin. Chem. Lett. In Press, 110995 (2025). https:\/\/doi.org\/10.1016\/j.cclet.2025.110995","journal-title":"Chin. Chem. Lett."},{"issue":"4","key":"1283_CR56","doi-asserted-by":"publisher","DOI":"10.3390\/children9040498","volume":"9","author":"K Lutz","year":"2022","unstructured":"Lutz, K., J\u00fcnger, S.T., Messing-J\u00fcnger, M.: Essential management of pediatric brain tumors. Children 9(4), 498 (2022). https:\/\/doi.org\/10.3390\/children9040498","journal-title":"Children"},{"key":"1283_CR57","doi-asserted-by":"publisher","unstructured":"E. A. Elgamal and R. M. Mohamed, \u201cPediatric brain tumors,\u201d in Springer eBooks, 2020, pp. 1033\u20131068. https:\/\/doi.org\/10.1007\/978-3-319-43153-6_35.","DOI":"10.1007\/978-3-319-43153-6_35"},{"issue":"2","key":"1283_CR58","doi-asserted-by":"publisher","first-page":"83","DOI":"10.1097\/rmr.0000000000000235","volume":"29","author":"FG Gon\u00e7alves","year":"2020","unstructured":"Gon\u00e7alves, F.G., Alves, C.A.P.F., Vossough, A.: Updates in pediatric malignant gliomas. Top. Magn. Reson. Imaging 29(2), 83\u201394 (2020). https:\/\/doi.org\/10.1097\/rmr.0000000000000235","journal-title":"Top. Magn. Reson. Imaging"},{"issue":"3","key":"1283_CR59","doi-asserted-by":"publisher","first-page":"431","DOI":"10.1007\/s00381-017-3687-4","volume":"34","author":"O Chamdine","year":"2018","unstructured":"Chamdine, O., Elhawary, G.A.S., Alfaar, A.S., Qaddoumi, I.: The incidence of brainstem primitive neuroectodermal tumors of childhood based on SEER data. Childs Nerv. Syst. 34(3), 431\u2013439 (2018). https:\/\/doi.org\/10.1007\/s00381-017-3687-4","journal-title":"Childs Nerv. Syst."},{"key":"1283_CR60","doi-asserted-by":"publisher","DOI":"10.1016\/j.neo.2022.100846","volume":"35","author":"JV Lilly","year":"2022","unstructured":"Lilly, J.V., et al.: The children\u2019s brain tumor network (CBTN) - Accelerating research in pediatric central nervous system tumors through collaboration and open science. Neoplasia 35, 100846 (2022). https:\/\/doi.org\/10.1016\/j.neo.2022.100846","journal-title":"Neoplasia"},{"key":"1283_CR61","unstructured":"Pediatric Brain Tumor Consortium Neuroimaging Center Research, \u201cPediatric Brain Tumor Consortium Neuroimaging Center,\u201d 2025. https:\/\/research.childrenshospital.org\/research-units\/pediatric-brain-tumor-consortium-neuroimaging-center-research (accessed Sep. 15, 2025)."},{"issue":"1","key":"1283_CR62","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1214\/23-aoas1781","volume":"18","author":"S Hwang","year":"2024","unstructured":"Hwang, S., Lee, T.C.M., Paul, D., Peng, J.: Estimating fiber orientation distribution with application to study brain lateralization using HCP D-MRI data. Ann. Appl. Stat. 18(1), 1\u201339 (2024). https:\/\/doi.org\/10.1214\/23-aoas1781","journal-title":"Ann. Appl. Stat."},{"issue":"16_Supplement","key":"1283_CR63","doi-asserted-by":"publisher","DOI":"10.1158\/1538-7445.am2020-6144","volume":"80","author":"PS Dunphy","year":"2020","unstructured":"Dunphy, P.S., et al.: Abstract 6144: St. Jude Pediatric Brain Tumor Portal: cloud-based resource for patient-derived orthotopic xenograft (PDOX) models of pediatric high-grade glioma, ependymoma, and CNS embryonal tumors. Cancer Res. 80(16_Supplement), 6144 (2020). https:\/\/doi.org\/10.1158\/1538-7445.am2020-6144","journal-title":"Cancer Res."},{"issue":"2","key":"1283_CR64","doi-asserted-by":"publisher","first-page":"209","DOI":"10.1007\/s00401-020-02171-5","volume":"140","author":"KS Smith","year":"2020","unstructured":"Smith, K.S., et al.: Patient-derived orthotopic xenografts of pediatric brain tumors: a St. Jude resource. Acta Neuropathol. 140(2), 209\u2013225 (2020). https:\/\/doi.org\/10.1007\/s00401-020-02171-5","journal-title":"Acta Neuropathol."},{"issue":"2","key":"1283_CR65","doi-asserted-by":"publisher","first-page":"211","DOI":"10.1007\/s12021-012-9164-z","volume":"11","author":"Y Dai","year":"2012","unstructured":"Dai, Y., Shi, F., Wang, L., Wu, G., Shen, D.: IBEAT: a toolbox for infant brain magnetic resonance image processing. Neuroinformatics 11(2), 211\u2013225 (2012). https:\/\/doi.org\/10.1007\/s12021-012-9164-z","journal-title":"Neuroinformatics"},{"key":"1283_CR66","doi-asserted-by":"publisher","DOI":"10.1038\/s41572-024-00516-y","author":"M Weller","year":"2024","unstructured":"Weller, M., et al.: Glioma. Nat. Rev. Dis. Primers. (2024). https:\/\/doi.org\/10.1038\/s41572-024-00516-y","journal-title":"Nat. Rev. Dis. Primers."},{"issue":"4","key":"1283_CR67","doi-asserted-by":"publisher","first-page":"903","DOI":"10.1007\/s10278-020-00347-9","volume":"33","author":"H Mzoughi","year":"2020","unstructured":"Mzoughi, H., et al.: Deep multi-scale 3D convolutional neural network (CNN) for MRI gliomas brain tumor classification. J. Digit. Imaging 33(4), 903\u2013915 (2020). https:\/\/doi.org\/10.1007\/s10278-020-00347-9","journal-title":"J. Digit. Imaging"},{"issue":"3","key":"1283_CR68","doi-asserted-by":"publisher","first-page":"293","DOI":"10.1080\/14737159.2020.1714439","volume":"20","author":"BK Li","year":"2020","unstructured":"Li, B.K., Al-Karmi, S., Huang, A., Bouffet, E.: Pediatric embryonal brain tumors in the molecular era. Expert Rev. Mol. Diagn. 20(3), 293\u2013303 (2020). https:\/\/doi.org\/10.1080\/14737159.2020.1714439","journal-title":"Expert Rev. Mol. Diagn."},{"issue":"178","key":"1283_CR69","doi-asserted-by":"publisher","first-page":"733","DOI":"10.24976\/discov.med.202335178.69","volume":"35","author":"L Shcherbina","year":"2023","unstructured":"Shcherbina, L., Kovalevska, E., Pedachenko, T., Malysheva, E., Kashuba, E.: Comparative analysis of the embryonal brain tumors based on their molecular features. Discov. Med. 35(178), 733 (2023). https:\/\/doi.org\/10.24976\/discov.med.202335178.69","journal-title":"Discov. Med."},{"issue":"1","key":"1283_CR70","doi-asserted-by":"publisher","first-page":"59","DOI":"10.1177\/10935266211018928","volume":"25","author":"M Santi","year":"2022","unstructured":"Santi, M., Viaene, A.N., Hawkins, C.: Ependymal tumors. Pediatr. Dev. Pathol. 25(1), 59\u201367 (2022). https:\/\/doi.org\/10.1177\/10935266211018928","journal-title":"Pediatr. Dev. Pathol."},{"key":"1283_CR71","doi-asserted-by":"publisher","first-page":"1","DOI":"10.3389\/fped.2023.1181211","volume":"11","author":"W Mu","year":"2023","unstructured":"Mu, W., Dahmoush, H.: Classification and neuroimaging of ependymal tumors. Front. Pediatr. 11, 1\u201312 (2023). https:\/\/doi.org\/10.3389\/fped.2023.1181211","journal-title":"Front. Pediatr."},{"key":"1283_CR72","doi-asserted-by":"publisher","first-page":"73","DOI":"10.1007\/978-3-031-23705-8_3","volume":"1405","author":"M Bailo","year":"2023","unstructured":"Bailo, M., et al.: Meningioma and other meningeal tumors. Adv. Exp. Med. Biol. 1405, 73\u201397 (2023). https:\/\/doi.org\/10.1007\/978-3-031-23705-8_3","journal-title":"Adv. Exp. Med. Biol."},{"issue":"12","key":"1283_CR73","doi-asserted-by":"publisher","first-page":"2612","DOI":"10.3390\/pharmaceutics14122612","volume":"14","author":"O Semyachkina-Glushkovskaya","year":"2022","unstructured":"Semyachkina-Glushkovskaya, O., et al.: Photodynamic opening of the Blood-Brain barrier and the meningeal lymphatic system: the new niche in immunotherapy for brain tumors. Pharmaceutics 14(12), 2612 (2022). https:\/\/doi.org\/10.3390\/pharmaceutics14122612","journal-title":"Pharmaceutics"},{"issue":"3","key":"1283_CR74","doi-asserted-by":"publisher","first-page":"269","DOI":"10.1177\/19714009211055195","volume":"35","author":"B Lubomirsky","year":"2021","unstructured":"Lubomirsky, B., Jenner, Z.B., Jude, M.B., Shahlaie, K., Assadsangabi, R., Ivanovic, V.: Sellar, suprasellar, and parasellar masses: Imaging features and neurosurgical approaches. Neuroradiol. J. 35(3), 269\u2013283 (2021). https:\/\/doi.org\/10.1177\/19714009211055195","journal-title":"Neuroradiol. J."},{"issue":"2","key":"1283_CR75","doi-asserted-by":"publisher","first-page":"305","DOI":"10.1016\/j.path.2020.02.006","volume":"13","author":"KE Schwetye","year":"2020","unstructured":"Schwetye, K.E., Dahiya, S.M.: Sellar tumors. Surgical Pathology Clinics 13(2), 305\u2013329 (2020). https:\/\/doi.org\/10.1016\/j.path.2020.02.006","journal-title":"Surgical Pathology Clinics"},{"issue":"2","key":"1283_CR76","doi-asserted-by":"publisher","first-page":"423","DOI":"10.1007\/s11060-022-04161-x","volume":"160","author":"H Jia","year":"2022","unstructured":"Jia, H., et al.: Brainstem tumors may increase the impairment of behavioral emotional cognition in children. J. Neurooncol. 160(2), 423\u2013432 (2022). https:\/\/doi.org\/10.1007\/s11060-022-04161-x","journal-title":"J. Neurooncol."},{"issue":"2","key":"1283_CR77","doi-asserted-by":"publisher","first-page":"297","DOI":"10.1007\/s00401-018-1936-6","volume":"137","author":"C Pan","year":"2018","unstructured":"Pan, C., et al.: Molecular profiling of tumors of the brainstem by sequencing of CSF-derived circulating tumor DNA. Acta Neuropathol. 137(2), 297\u2013306 (2018). https:\/\/doi.org\/10.1007\/s00401-018-1936-6","journal-title":"Acta Neuropathol."},{"key":"1283_CR78","doi-asserted-by":"publisher","first-page":"12870","DOI":"10.1109\/access.2023.3242666","volume":"11","author":"S Solanki","year":"2023","unstructured":"Solanki, S., Singh, U.P., Chouhan, S.S., Jain, S.: Brain Tumor Detection and Classification Using Intelligence Techniques: An Overview. IEEE Access 11, 12870\u201312886 (Jan.2023). https:\/\/doi.org\/10.1109\/access.2023.3242666","journal-title":"IEEE Access"},{"key":"1283_CR79","doi-asserted-by":"publisher","DOI":"10.2196\/57723","volume":"27","author":"M Iratni","year":"2025","unstructured":"Iratni, M., et al.: Transformers for Neuroimage Segmentation: Scoping Review. J. Med. Internet Res. 27, e57723 (2025). https:\/\/doi.org\/10.2196\/57723","journal-title":"J. Med. Internet Res."},{"issue":"1","key":"1283_CR80","doi-asserted-by":"publisher","first-page":"75","DOI":"10.1016\/j.mcpdig.2024.01.002","volume":"2","author":"T-W Wang","year":"2024","unstructured":"Wang, T.-W., et al.: Artificial Intelligence Detection and Segmentation Models: A Systematic Review and Meta-Analysis of Brain Tumors in Magnetic Resonance Imaging. Mayo Clinic Proceedings Digital Health 2(1), 75\u201391 (2024). https:\/\/doi.org\/10.1016\/j.mcpdig.2024.01.002","journal-title":"Mayo Clinic Proceedings Digital Health"},{"key":"1283_CR81","doi-asserted-by":"publisher","unstructured":"F. Isensee, P. F. J\u00e4ger, P. M. Full, P. Vollmuth, and K. H. Maier-Hein: NNU-NeT for Brain Tumor Segmentation. in Lecture notes in computer science pp. 118\u2013132 (2021). https:\/\/doi.org\/10.1007\/978-3-030-72087-2_11","DOI":"10.1007\/978-3-030-72087-2_11"},{"issue":"8","key":"1283_CR82","doi-asserted-by":"publisher","first-page":"2451","DOI":"10.1109\/tmi.2023.3250474","volume":"42","author":"J Lin","year":"2023","unstructured":"Lin, J., et al.: CKD-TrANSBTS: Clinical Knowledge-Driven hybrid transformer with Modality-Correlated Cross-Attention for brain tumor segmentation. IEEE Trans. Med. Imaging 42(8), 2451\u20132461 (2023). https:\/\/doi.org\/10.1109\/tmi.2023.3250474","journal-title":"IEEE Trans. Med. Imaging"},{"key":"1283_CR83","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2024.126290","volume":"268","author":"E Goceri","year":"2024","unstructured":"Goceri, E.: An efficient network with CNN and transformer blocks for glioma grading and brain tumor classification from MRIs. Expert Syst. Appl. 268, 126290 (2024). https:\/\/doi.org\/10.1016\/j.eswa.2024.126290","journal-title":"Expert Syst. Appl."},{"key":"1283_CR84","doi-asserted-by":"publisher","unstructured":"D. Liu, Y. Liu, and L. Dong: G-RESNET Improved RESNeT for brain tumor Classification,\u201d in Lecture notes in computer science, 1st ed 1, pp. 535\u2013545 (2019). https:\/\/doi.org\/10.1007\/978-3-030-36708-4_44","DOI":"10.1007\/978-3-030-36708-4_44"},{"issue":"14","key":"1283_CR85","doi-asserted-by":"publisher","DOI":"10.3390\/app12147282","volume":"12","author":"L Younis","year":"2022","unstructured":"Younis, L., Qiang, C.O., Nyatega, M.J., Adamu, H.B., Kawuwa, H.B.: Brain tumor analysis using deep learning and VGG-16 ensembling learning approaches. Appl. Sci. 12(14), 7282 (2022). https:\/\/doi.org\/10.3390\/app12147282","journal-title":"Appl. Sci."},{"issue":"3","key":"1283_CR86","doi-asserted-by":"publisher","first-page":"703","DOI":"10.1007\/s13246-021-01019-w","volume":"44","author":"S Ghosh","year":"2021","unstructured":"Ghosh, S., Chaki, A., Santosh, K.: Improved U-Net architecture with VGG-16 for brain tumor segmentation. Phys. Eng. Sci. Med. 44(3), 703\u2013712 (2021). https:\/\/doi.org\/10.1007\/s13246-021-01019-w","journal-title":"Phys. Eng. Sci. Med."},{"issue":"1","key":"1283_CR87","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0286125","volume":"19","author":"X Wu","year":"2024","unstructured":"Wu, X., Yang, X., Li, Z., Liu, L., Xia, Y.: Multimodal brain tumor image segmentation based on DenseNet. PLoS ONE 19(1), e0286125 (2024). https:\/\/doi.org\/10.1371\/journal.pone.0286125","journal-title":"PLoS ONE"},{"issue":"08","key":"1283_CR88","doi-asserted-by":"publisher","first-page":"97","DOI":"10.3991\/ijoe.v19i08.38619","volume":"19","author":"TATK Azaharan","year":"2023","unstructured":"Azaharan, T.A.T.K., Mahamad, A.K., Saon, S., Muladi, N., Mudjanarko, S.W.: Investigation of VGG-16, ReSNet-50 and AlexNet performance for brain tumor detection. International Journal of Online and Biomedical Engineering (iJOE) 19(08), 97\u2013109 (2023). https:\/\/doi.org\/10.3991\/ijoe.v19i08.38619","journal-title":"International Journal of Online and Biomedical Engineering (iJOE)"},{"issue":"1","key":"1283_CR89","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s12880-024-01292-7","volume":"24","author":"MM M","year":"2024","unstructured":"M, M.M., R, M.T., Kumar, V.V., Guluwadi, S.: Enhancing brain tumor detection in MRI images through explainable AI using Grad-CAM with Resnet 50. BMC Med. Imaging 24(1), 1\u201319 (2024). https:\/\/doi.org\/10.1186\/s12880-024-01292-7","journal-title":"BMC Med. Imaging"},{"issue":"16","key":"1283_CR90","doi-asserted-by":"publisher","DOI":"10.3390\/cancers15164172","volume":"15","author":"B Abdusalomov","year":"2023","unstructured":"Abdusalomov, B., Mukhiddinov, M., Whangbo, T.K.: Brain tumor detection based on deep learning approaches and magnetic resonance imaging. Cancers 15(16), 4172 (2023). https:\/\/doi.org\/10.3390\/cancers15164172","journal-title":"Cancers"},{"issue":"4","key":"1283_CR91","doi-asserted-by":"publisher","first-page":"1581","DOI":"10.18280\/ts.400426","volume":"40","author":"G Srinivasarao","year":"2023","unstructured":"Srinivasarao, G., Rajesh, V., Saikumar, K., Baza, M., Srivastava, G., Alsabaan, M.: Cloud-based LENET-5 CNN for MRI brain tumor diagnosis and recognition. Trait. Signal 40(4), 1581\u20131592 (2023). https:\/\/doi.org\/10.18280\/ts.400426","journal-title":"Trait. Signal"},{"key":"1283_CR92","doi-asserted-by":"publisher","DOI":"10.1007\/978-981-97-8329-8_66","author":"M Sharma","year":"2025","unstructured":"Sharma, M., Singh, S., Singh, S.: Role of capsule network model in brain tumor analysis and detection: a review. Springer (2025). https:\/\/doi.org\/10.1007\/978-981-97-8329-8_66","journal-title":"Springer"},{"issue":"1","key":"1283_CR93","doi-asserted-by":"publisher","DOI":"10.1007\/s12559-024-10387-w","volume":"17","author":"A Choudhry","year":"2024","unstructured":"Choudhry, A., Iqbal, S., Alhussein, M., Aurangzeb, K., Qureshi, A.N., Hussain, A.: A novel interpretable graph convolutional neural network for multimodal brain tumor segmentation. Cogn. Comput. 17(1), 12559 (2024). https:\/\/doi.org\/10.1007\/s12559-024-10387-w","journal-title":"Cogn. Comput."},{"key":"1283_CR94","doi-asserted-by":"publisher","first-page":"4896","DOI":"10.1109\/tip.2024.3451936","volume":"33","author":"T Zhou","year":"2024","unstructured":"Zhou, T.: M2GCNET: multi-modal graph convolution network for precise brain tumor segmentation across multiple MRI sequences. IEEE Trans. Image Process. 33, 4896\u20134910 (2024). https:\/\/doi.org\/10.1109\/tip.2024.3451936","journal-title":"IEEE Trans. Image Process."},{"key":"1283_CR95","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2024.106137","volume":"92","author":"Peng","year":"2024","unstructured":"Peng, et al.: Multi-Level fusion graph neural network: Application to PET and CT imaging for risk stratification of head and neck cancer. Biomed. Signal Process. Control 92, 106137 (Feb.2024). https:\/\/doi.org\/10.1016\/j.bspc.2024.106137","journal-title":"Biomed. Signal Process. Control"},{"issue":"2","key":"1283_CR96","doi-asserted-by":"publisher","DOI":"10.3390\/bdcc9020029","volume":"9","author":"P Joshi","year":"2025","unstructured":"Joshi, P., Gowda, V.B., Divakarachari, P.B., Parameshwarappa, P.S., Patra, R.K.: VSA-GCNN: attention guided graph neural networks for brain tumor segmentation and classification. Big Data Cogn. Comput. 9(2), 29 (2025). https:\/\/doi.org\/10.3390\/bdcc9020029","journal-title":"Big Data Cogn. Comput."},{"issue":"1","key":"1283_CR97","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s12911-025-03029-0","volume":"25","author":"J Yu","year":"2025","unstructured":"Yu, J., et al.: Deep learning-driven modality imputation and subregion segmentation to enhance high-grade glioma grading. BMC Med. Inform. Decis. Mak. 25(1), 1\u201316 (2025). https:\/\/doi.org\/10.1186\/s12911-025-03029-0","journal-title":"BMC Med. Inform. Decis. Mak."},{"key":"1283_CR98","doi-asserted-by":"publisher","DOI":"10.7717\/peerj-cs.1878","volume":"10","author":"A Asiri","year":"2024","unstructured":"Asiri, A., Shaf, A., Ali, T., Aamir, M., Irfan, M., Alqahtani, S.: Enhancing brain tumor diagnosis: an optimized CNN hyperparameter model for improved accuracy and reliability. PeerJ Comput. Sci. 10, e1878 (2024). https:\/\/doi.org\/10.7717\/peerj-cs.1878","journal-title":"PeerJ Comput. Sci."},{"issue":"1","key":"1283_CR99","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s12880-024-01285-6","volume":"24","author":"Alshuhail","year":"2024","unstructured":"Alshuhail, et al.: Refining neural network algorithms for accurate brain tumor classification in MRI imagery. BMC Med. Imaging 24(1), 1\u201320 (2024). https:\/\/doi.org\/10.1186\/s12880-024-01285-6","journal-title":"BMC Med. Imaging"},{"issue":"7","key":"1283_CR100","doi-asserted-by":"publisher","first-page":"2165","DOI":"10.1007\/s11517-024-03064-5","volume":"62","author":"Y Ozdemir","year":"2024","unstructured":"Ozdemir, Y., Dogan, Y.: Advancing brain tumor classification through MTAP model: an innovative approach in medical diagnostics. Med. Biol. Eng. Comput. 62(7), 2165\u20132176 (2024). https:\/\/doi.org\/10.1007\/s11517-024-03064-5","journal-title":"Med. Biol. Eng. Comput."},{"key":"1283_CR101","doi-asserted-by":"publisher","first-page":"107409","DOI":"10.1016\/j.bspc.2024.107409","volume":"103","author":"M Barati","year":"2024","unstructured":"Barati, M., Erfaninejad, M., Khanbabaei, H.: Evaluation of effect of optimizers and loss functions on prediction accuracy of brain tumor type using a light neural network. Biomed. Signal Process. Control 103, 107409 (2024). https:\/\/doi.org\/10.1016\/j.bspc.2024.107409","journal-title":"Biomed. Signal Process. Control"},{"key":"1283_CR102","doi-asserted-by":"publisher","unstructured":"H. Nguyen-Truong and Q.-D. Pham:Dice Focal Loss with ResNet-like Encoder-Decoder Architecture in 3D Brain Tumor Segmentation in Lecture notes in computer science, Springer, pp. 97\u2013105 (2022). https:\/\/doi.org\/10.1007\/978-3-031-09002-8_9","DOI":"10.1007\/978-3-031-09002-8_9"},{"issue":"7","key":"1283_CR103","doi-asserted-by":"publisher","first-page":"4325","DOI":"10.1002\/mp.16244","volume":"50","author":"B Li","year":"2023","unstructured":"Li, B., You, X., Peng, Q., Wang, J., Yang, C.: Region\u2010related focal loss for 3D brain tumor MRI segmentation. Med. Phys. 50(7), 4325\u20134339 (2023). https:\/\/doi.org\/10.1002\/mp.16244","journal-title":"Med. Phys."},{"issue":"1","key":"1283_CR104","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41598-025-07257-2","volume":"15","author":"E Cariola","year":"2025","unstructured":"Cariola, E., Sibilano, A., Guerriero, V., Bevilacqua, A., Brunetti, A.: Deep learning strategies for semantic segmentation of pediatric brain tumors in multiparametric MRI. Sci. Rep. 15(1), 1\u201315 (2025). https:\/\/doi.org\/10.1038\/s41598-025-07257-2","journal-title":"Sci. Rep."},{"key":"1283_CR105","doi-asserted-by":"publisher","first-page":"32681","DOI":"10.1109\/access.2025.3543214","volume":"13","author":"SK Chhotray","year":"2025","unstructured":"Chhotray, S.K., Mishra, D., Pati, S.P., Mishra, S.: An optimized cascaded CNN approach for feature extraction from brain MRIs for tumor classification. IEEE Access 13, 32681\u201332705 (2025). https:\/\/doi.org\/10.1109\/access.2025.3543214","journal-title":"IEEE Access"},{"issue":"10","key":"1283_CR106","doi-asserted-by":"publisher","first-page":"1727","DOI":"10.1007\/s11548-021-02471-5","volume":"16","author":"J Wang","year":"2021","unstructured":"Wang, J., et al.: Coarse-to-fine multiplanar D-SEA UNet for automatic 3D carotid segmentation in CTA images. Int. J. Comput. Assist. Radiol. Surg. 16(10), 1727\u20131736 (2021). https:\/\/doi.org\/10.1007\/s11548-021-02471-5","journal-title":"Int. J. Comput. Assist. Radiol. Surg."},{"issue":"5","key":"1283_CR107","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1002\/ima.23152","volume":"34","author":"H Saleh","year":"2024","unstructured":"Saleh, H., Atila, \u00dc., Menemencio\u011flu, O.: Multimodal fusion for enhanced semantic segmentation in brain tumor imaging: integrating deep learning and guided filtering via advanced 3D semantic segmentation architectures. Int. J. Imaging Syst. Technol. 34(5), 1\u201320 (2024). https:\/\/doi.org\/10.1002\/ima.23152","journal-title":"Int. J. Imaging Syst. Technol."},{"key":"1283_CR108","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2023.104834","volume":"85","author":"Z Sobhaninia","year":"2023","unstructured":"Sobhaninia, Z., Karimi, N., Khadivi, P., Samavi, S.: Brain tumor segmentation by cascaded multiscale multitask learning framework based on feature aggregation. Biomed. Signal Process. Control 85, 104834 (2023). https:\/\/doi.org\/10.1016\/j.bspc.2023.104834","journal-title":"Biomed. Signal Process. Control"},{"issue":"1","key":"1283_CR109","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41598-025-92776-1","volume":"15","author":"AA Zarenia","year":"2025","unstructured":"Zarenia, A.A., Far, A., Rezaee, K.: Automated multi-class MRI brain tumor classification and segmentation using deformable attention and saliency mapping. Sci. Rep. 15(1), 1\u201327 (2025). https:\/\/doi.org\/10.1038\/s41598-025-92776-1","journal-title":"Sci. Rep."},{"issue":"12","key":"1283_CR110","doi-asserted-by":"publisher","DOI":"10.3390\/healthcare10122340","volume":"10","author":"NA Samee","year":"2022","unstructured":"Samee, N.A., Ahmad, T., Mahmoud, N.F., Atteia, G., Abdallah, H.A., Rizwan, A.: Clinical decision support framework for segmentation and classification of brain tumor MRIs using a U-Net and DCNN cascaded learning algorithm. Healthcare (Basel) 10(12), 2340 (2022). https:\/\/doi.org\/10.3390\/healthcare10122340","journal-title":"Healthcare (Basel)"},{"key":"1283_CR111","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2024.106550","volume":"96","author":"Karthik","year":"2024","unstructured":"Karthik, et al.: Ensemble-based multimodal medical imaging fusion for tumor segmentation. Biomed. Signal Process. Control 96, 106550 (2024). https:\/\/doi.org\/10.1016\/j.bspc.2024.106550","journal-title":"Biomed. Signal Process. Control"},{"key":"1283_CR112","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejmp.2024.104841","volume":"127","author":"A Ali","year":"2024","unstructured":"Ali, A., Dornaika, F., Arganda-Carreras, I., Chmouri, R., Shayeh, H.: Enhancing MRI brain tumor classification: a comprehensive approach integrating real-life scenario simulation and augmentation techniques. Phys. Med. 127, 104841 (2024). https:\/\/doi.org\/10.1016\/j.ejmp.2024.104841","journal-title":"Phys. Med."},{"key":"1283_CR113","doi-asserted-by":"publisher","first-page":"100407","DOI":"10.1109\/access.2024.3430109","volume":"12","author":"Shamshad","year":"2024","unstructured":"Shamshad, et al.: Enhancing brain tumor classification by a comprehensive study on transfer learning techniques and model efficiency using MRI datasets. IEEE Access 12, 100407\u2013100418 (2024). https:\/\/doi.org\/10.1109\/access.2024.3430109","journal-title":"IEEE Access"},{"issue":"1","key":"1283_CR114","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41598-024-57970-7","volume":"14","author":"SK Mathivanan","year":"2024","unstructured":"Mathivanan, S.K., Sonaimuthu, S., Murugesan, S., Rajadurai, H., Shivahare, B.D., Shah, M.A.: Employing deep learning and transfer learning for accurate brain tumor detection. Sci. Rep. 14(1), 1\u201315 (2024). https:\/\/doi.org\/10.1038\/s41598-024-57970-7","journal-title":"Sci. Rep."},{"issue":"1","key":"1283_CR115","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41598-025-92020-w","volume":"15","author":"A Ilani","year":"2025","unstructured":"Ilani, A., Shi, D., Banad, Y.M.: T1-weighted MRI-based brain tumor classification using hybrid deep learning models. Sci. Rep. 15(1), 1\u201316 (2025). https:\/\/doi.org\/10.1038\/s41598-025-92020-w","journal-title":"Sci. Rep."},{"key":"1283_CR116","doi-asserted-by":"publisher","unstructured":"Obenaus and J. Badaut:Role of the non\u2010invasive imaging techniques in monitoring and understanding the evolution of brain edema. Journal of Neuroscience Research, 100(5),1191\u20131200 (2021). https:\/\/doi.org\/10.1002\/jnr.24837","DOI":"10.1002\/jnr.24837"},{"key":"1283_CR117","doi-asserted-by":"publisher","unstructured":"M. Tabassum, P. Rana, E. S. Molina, A. Di Ieva, and S. Liu:Cross-Modality Synthesis of T1c MRI from Non-contrast Images Using GANs. Implications for Brain Tumor Research,\u201d in Lecture notes in computer science pp. 60\u201369 (2024). https:\/\/doi.org\/10.1007\/978-3-031-66535-6_7","DOI":"10.1007\/978-3-031-66535-6_7"},{"issue":"4","key":"1283_CR118","doi-asserted-by":"publisher","first-page":"531","DOI":"10.1007\/s00234-024-03319-w","volume":"66","author":"X Liu","year":"2024","unstructured":"Liu, X., et al.: Whole-tumor histogram analysis of postcontrast T1-weighted and apparent diffusion coefficient in predicting the grade and proliferative activity of adult intracranial ependymomas. Neuroradiology 66(4), 531\u2013541 (2024). https:\/\/doi.org\/10.1007\/s00234-024-03319-w","journal-title":"Neuroradiology"},{"issue":"5","key":"1283_CR119","doi-asserted-by":"publisher","first-page":"1182","DOI":"10.1016\/j.ijrobp.2024.11.073","volume":"121","author":"JM Karp","year":"2025","unstructured":"Karp, J.M., Kruser, T.J.: Contouring with FLAIR: targeting peritumoral edema (and beyond) in Glioblastoma. Int. J. Radiat. Oncol. Biol. Phys. 121(5), 1182\u20131184 (2025). https:\/\/doi.org\/10.1016\/j.ijrobp.2024.11.073","journal-title":"Int. J. Radiat. Oncol. Biol. Phys."},{"key":"1283_CR120","doi-asserted-by":"publisher","unstructured":"Ghafourian, F. Samadifam, H. Fadavian, P. J. Canatalay, A. Tajally, and S. Channumsin:An Ensemble Model for the Diagnosis of Brain Tumors through MRIs.Diagnostics13(3), p. 561 (2023). https:\/\/doi.org\/10.3390\/diagnostics13030561","DOI":"10.3390\/diagnostics13030561"},{"key":"1283_CR121","doi-asserted-by":"publisher","unstructured":"Singh, R. Chitalia, and D. Kontos:Radiogenomics in brain, breast, and lung cancer opportunities and challenges.Journal of Medical Imaging 8(03), 1\u201314 (2021). https:\/\/doi.org\/10.1117\/1.jmi.8.3.031907","DOI":"10.1117\/1.jmi.8.3.031907"},{"key":"1283_CR122","doi-asserted-by":"publisher","unstructured":"M. Varma, P. Rakesh, V. Hemanth, B. Mukesh, and A. J: Uncertainty-Aware Brain Tumor Segmentation in MRI: A Res-UNet Framework with Monte Carlo Dropout. IEEE ICDICI\u20192025, 2025. https:\/\/doi.org\/10.1109\/icdici66477.2025.11135391","DOI":"10.1109\/icdici66477.2025.11135391"},{"issue":"3","key":"1283_CR123","doi-asserted-by":"publisher","DOI":"10.3390\/healthcare10030494","volume":"10","author":"MA Amou","year":"2022","unstructured":"Amou, M.A., Xia, K., Kamhi, S., Mouhafid, M.: A novel MRI diagnosis method for brain tumor classification based on CNN and Bayesian optimization. Healthcare 10(3), 494 (2022). https:\/\/doi.org\/10.3390\/healthcare10030494","journal-title":"Healthcare"},{"issue":"8","key":"1283_CR124","doi-asserted-by":"publisher","first-page":"666","DOI":"10.1016\/j.trecan.2021.06.003","volume":"7","author":"XAMH Van Dierendonck","year":"2021","unstructured":"Van Dierendonck, X.A.M.H., De Goede, K.E., Van Den Bossche, J.: IDH-Mutant brain tumors hit the Achilles\u2019 Heel of macrophages with R-2-Hydroxyglutarate. Trends Cancer 7(8), 666\u2013667 (2021). https:\/\/doi.org\/10.1016\/j.trecan.2021.06.003","journal-title":"Trends Cancer"},{"key":"1283_CR125","doi-asserted-by":"publisher","DOI":"10.1007\/s10586-024-04940-3","author":"SUR Khan","year":"2025","unstructured":"Khan, S.U.R., Zhao, M., Li, Y.: Detection of MRI brain tumor using residual skip block based modified MobileNet model. Cluster Comput. (2025). https:\/\/doi.org\/10.1007\/s10586-024-04940-3","journal-title":"Cluster Comput."},{"key":"1283_CR126","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2025.113775","volume":"184","author":"S Rao","year":"2025","unstructured":"Rao, S., Bavirthi, S.S., Sharada, G., Ranganath, P., Sailaja, V., Kumari, G.V.: Hybrid Spinalnet-Fuzzy-Shufflenet for brain tumor detection using MRI images. Appl. Soft Comput. 184, 113775 (2025). https:\/\/doi.org\/10.1016\/j.asoc.2025.113775","journal-title":"Appl. Soft Comput."},{"issue":"6","key":"1283_CR127","doi-asserted-by":"publisher","DOI":"10.1002\/nbm.70035","volume":"38","author":"SS Shinde","year":"2025","unstructured":"Shinde, S.S., Pande, A.: High\u2010performance computing\u2010based brain tumor detection using parallel quantum dilated convolutional neural network. NMR Biomed. 38(6), e70035 (2025). https:\/\/doi.org\/10.1002\/nbm.70035","journal-title":"NMR Biomed."},{"key":"1283_CR128","doi-asserted-by":"publisher","first-page":"4749","DOI":"10.1007\/s41870-024-02216-y","volume":"16","author":"S Poornam","year":"2024","unstructured":"Poornam, S., Angelina, J.J.R.: BrainNeuroNet: advancing brain tumor detection with hierarchical transformers and multiscale attention. Int. J. Inf. Technol. 16, 4749\u20134756 (2024). https:\/\/doi.org\/10.1007\/s41870-024-02216-y","journal-title":"Int. J. Inf. Technol."},{"key":"1283_CR129","doi-asserted-by":"publisher","unstructured":"Mhaouch, W. Gtifa, T. Althobaiti, H. Faraj, and M. Machhout:A quality of service analysis of FPGA-Accelerated CONV2D architectures for Brain Tumor Multi-Classification,\u201d Computers, Materials & Continua\/Computers, Materials & Continua (Print), vol. 84(3), 1\u201327 (2025). https:\/\/doi.org\/10.32604\/cmc.2025.065525","DOI":"10.32604\/cmc.2025.065525"},{"key":"1283_CR130","doi-asserted-by":"publisher","first-page":"1","DOI":"10.3389\/fnins.2022.916818","volume":"16","author":"J Zhou","year":"2022","unstructured":"Zhou, J., et al.: SCSE-NL V-NET: A brain tumor automatic segmentation method based on spatial and channel \u2018Squeeze-and-Excitation\u2019 network with non-local block. Front. Neurosci. 16, 1\u201315 (2022). https:\/\/doi.org\/10.3389\/fnins.2022.916818","journal-title":"Front. Neurosci."},{"issue":"8","key":"1283_CR131","doi-asserted-by":"publisher","first-page":"1081","DOI":"10.3174\/ajnr.a8293","volume":"45","author":"Vossough","year":"2024","unstructured":"Vossough, et al.: Training and comparison of NNU-Net and DeepMedic methods for autosegmentation of pediatric brain tumors. Am. J. Neuroradiol. 45(8), 1081\u20131089 (2024). https:\/\/doi.org\/10.3174\/ajnr.a8293","journal-title":"Am. J. Neuroradiol."},{"issue":"3","key":"1283_CR132","doi-asserted-by":"publisher","first-page":"857","DOI":"10.1002\/ima.22677","volume":"32","author":"S Cui","year":"2021","unstructured":"Cui, S., Wei, M., Liu, C., Jiang, J.: GAN\u2010segNet: A deep generative adversarial segmentation network for brain tumor semantic segmentation. Int. J. Imaging Syst. Technol. 32(3), 857\u2013868 (2021). https:\/\/doi.org\/10.1002\/ima.22677","journal-title":"Int. J. Imaging Syst. Technol."},{"key":"1283_CR133","doi-asserted-by":"publisher","first-page":"34","DOI":"10.1016\/j.neucom.2020.10.031","volume":"423","author":"J Sun","year":"2020","unstructured":"Sun, J., Peng, Y., Guo, Y., Li, D.: Segmentation of the multimodal brain tumor image used the multi-pathway architecture method based on 3D FCN. Neurocomputing 423, 34\u201345 (2020). https:\/\/doi.org\/10.1016\/j.neucom.2020.10.031","journal-title":"Neurocomputing"},{"issue":"8","key":"1283_CR134","doi-asserted-by":"publisher","DOI":"10.3390\/app14083424","volume":"14","author":"S Mohammadi","year":"2024","unstructured":"Mohammadi, S., Allali, M.: Advancing brain tumor segmentation with spectral\u2013spatial graph neural networks. Appl. Sci. (Basel) 14(8), 3424 (2024). https:\/\/doi.org\/10.3390\/app14083424","journal-title":"Appl. Sci. (Basel)"},{"issue":"1","key":"1283_CR135","doi-asserted-by":"publisher","DOI":"10.3390\/brainsci15010017","volume":"15","author":"H Mohammadi","year":"2024","unstructured":"Mohammadi, H., Karwowski, W.: Graph neural networks in brain connectivity studies: methods, challenges, and future directions. Brain Sci. 15(1), 17 (2024). https:\/\/doi.org\/10.3390\/brainsci15010017","journal-title":"Brain Sci."},{"key":"1283_CR136","doi-asserted-by":"publisher","unstructured":"Hatamizadeh, V. Nath, Y. Tang, D. Yang, H. R. Roth, and D. Xu:SWIN UNETR: SWIN Transformers for Semantic Segmentation of Brain Tumors in MRI images in Lecture notes in computer science, pp. 272\u2013284 (2022). https:\/\/doi.org\/10.1007\/978-3-031-08999-2_22","DOI":"10.1007\/978-3-031-08999-2_22"},{"issue":"2","key":"1283_CR137","doi-asserted-by":"publisher","first-page":"273","DOI":"10.1007\/s11548-023-03024-8","volume":"19","author":"PV Ghazouani","year":"2023","unstructured":"Ghazouani, P.V., Ruan, S.: Efficient brain tumor segmentation using Swin transformer and enhanced local self-attention. Int. J. Comput. Assist. Radiol. Surg. 19(2), 273\u2013281 (2023). https:\/\/doi.org\/10.1007\/s11548-023-03024-8","journal-title":"Int. J. Comput. Assist. Radiol. Surg."},{"issue":"1","key":"1283_CR138","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41598-024-67554-0","volume":"14","author":"R Xu","year":"2024","unstructured":"Xu, R., Xu, C., Li, Z., Zheng, T., Yu, W., Yang, C.: Boundary guidance network for medical image segmentation. Sci. Rep. 14(1), 1\u201314 (2024). https:\/\/doi.org\/10.1038\/s41598-024-67554-0","journal-title":"Sci. Rep."},{"key":"1283_CR139","doi-asserted-by":"publisher","first-page":"133392","DOI":"10.1109\/access.2024.3460797","volume":"12","author":"L Liu","year":"2024","unstructured":"Liu, L., Xia, K.: BTIS-Net: Efficient 3D U-Net for brain tumor image segmentation. IEEE Access 12, 133392\u2013133405 (2024). https:\/\/doi.org\/10.1109\/access.2024.3460797","journal-title":"IEEE Access"},{"key":"1283_CR140","unstructured":"The Cancer Imaging Archive (TCIA) TCGA-LGG - The Cancer Imaging Archive (TCIA). The Cancer Imaging Archive (TCIA), (2025). https:\/\/www.cancerimagingarchive.net\/collection\/tcga-lgg\/#citations (accessed 15 Sep 2025)"},{"issue":"1","key":"1283_CR141","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/sdata.2018.158","volume":"5","author":"Y Gusev","year":"2018","unstructured":"Gusev, Y., Bhuvaneshwar, K., Song, L., Zenklusen, J.-C., Fine, H., Madhavan, S.: The REMBRANDT study, a large collection of genomic data from brain cancer patients. Scientific Data 5(1), 1\u20139 (2018). https:\/\/doi.org\/10.1038\/sdata.2018.158","journal-title":"Scientific Data"},{"key":"1283_CR142","unstructured":"Figshare:Brain tumor dataset Figshare (2024). https:\/\/figshare.com\/articles\/dataset\/brain_tumor_dataset\/1512427 (accessed 15 Sep 2025)"},{"issue":"5","key":"1283_CR143","doi-asserted-by":"publisher","DOI":"10.3390\/eng6050082","volume":"6","author":"N Musthafa","year":"2025","unstructured":"Musthafa, N., Memon, Q.A., Masud, M.M.: Advancing brain tumor analysis: current trends, key challenges, and perspectives in deep learning-based brain MRI tumor diagnosis. Eng 6(5), 82 (2025). https:\/\/doi.org\/10.3390\/eng6050082","journal-title":"Eng"},{"key":"1283_CR144","doi-asserted-by":"publisher","first-page":"1","DOI":"10.3389\/fninf.2022.919779","volume":"16","author":"N Saat","year":"2022","unstructured":"Saat, N., Nogovitsyn, M., Hassan, M.Y., Ganaie, M.A., Souza, R., Hemmati, H.: A domain adaptation benchmark for T1-weighted brain magnetic resonance image segmentation. Front. Neuroinform. 16, 1\u201319 (2022). https:\/\/doi.org\/10.3389\/fninf.2022.919779","journal-title":"Front. Neuroinform."},{"issue":"18","key":"1283_CR145","doi-asserted-by":"publisher","DOI":"10.3390\/diagnostics13182947","volume":"13","author":"AH Kushol","year":"2023","unstructured":"Kushol, A.H., Wilman, S., Kalra, S., Yang, Y.-H.: DSMRI: domain shift analyzer for multi-center MRI datasets. Diagnostics 13(18), 2947 (2023). https:\/\/doi.org\/10.3390\/diagnostics13182947","journal-title":"Diagnostics"},{"issue":"7","key":"1283_CR146","doi-asserted-by":"publisher","first-page":"1421","DOI":"10.3174\/ajnr.a8805","volume":"46","author":"M Von Reppert","year":"2025","unstructured":"Von Reppert, M., et al.: Image-based search in Radiology: identification of brain tumor subtypes within databases using MRI-based radiomic features. AJNR Am. J. Neuroradiol. 46(7), 1421\u20131428 (2025). https:\/\/doi.org\/10.3174\/ajnr.a8805","journal-title":"AJNR Am. J. Neuroradiol."},{"issue":"34","key":"1283_CR147","doi-asserted-by":"publisher","first-page":"21465","DOI":"10.1007\/s00521-024-10347-3","volume":"36","author":"K Singh","year":"2024","unstructured":"Singh, K., Malhotra, D.: IRAM\u2013NET model: image residual agnostics meta-learning-based network for rare de novo glioblastoma diagnosis. Neural Comput. & Appl. 36(34), 21465\u201321485 (2024). https:\/\/doi.org\/10.1007\/s00521-024-10347-3","journal-title":"Neural Comput. & Appl."},{"issue":"17","key":"1283_CR148","doi-asserted-by":"publisher","DOI":"10.3390\/cancers17172853","volume":"17","author":"C Kakon","year":"2025","unstructured":"Kakon, C., Sazid, Z.A., Begum, I.A., Samad, M.A., Hosen, A.S.M.S.: Explainable deep ensemble meta-learning framework for brain tumor classification using MRI images. Cancers 17(17), 2853 (2025). https:\/\/doi.org\/10.3390\/cancers17172853","journal-title":"Cancers"},{"issue":"1","key":"1283_CR149","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s12880-022-00812-7","volume":"22","author":"D Battalapalli","year":"2022","unstructured":"Battalapalli, D., Rao, B.V.V.S.N.P., Yogeeswari, P., Kesavadas, C., Rajagopalan, V.: An optimal brain tumor segmentation algorithm for clinical MRI dataset with low resolution and non-contiguous slices. BMC Med. Imaging 22(1), 1\u201312 (2022). https:\/\/doi.org\/10.1186\/s12880-022-00812-7","journal-title":"BMC Med. Imaging"},{"issue":"2","key":"1283_CR150","doi-asserted-by":"publisher","DOI":"10.3390\/jimaging7020031","volume":"7","author":"P Zhang","year":"2021","unstructured":"Zhang, P., Li, J., Wang, Y., Pan, J.: Domain adaptation for medical image segmentation: a meta-learning method. J. Imaging 7(2), 31 (2021). https:\/\/doi.org\/10.3390\/jimaging7020031","journal-title":"J. Imaging"},{"issue":"3","key":"1283_CR151","doi-asserted-by":"publisher","first-page":"305","DOI":"10.1007\/s10548-023-00953-0","volume":"36","author":"S Kumar","year":"2023","unstructured":"Kumar, S., Choudhary, A., Jain, A., Singh, K., Ahmadian, A., Bajuri, M.Y.: Brain tumor classification using deep neural network and transfer learning. Brain Topogr. 36(3), 305\u2013318 (2023). https:\/\/doi.org\/10.1007\/s10548-023-00953-0","journal-title":"Brain Topogr."},{"key":"1283_CR152","doi-asserted-by":"publisher","first-page":"131583","DOI":"10.1109\/access.2021.3112996","volume":"9","author":"V Singh","year":"2021","unstructured":"Singh, V., Jaiswal, G., Joshi, A., Sanjeeve, A., Gite, S., Kotecha, K.: Neural style transfer: a critical review. IEEE Access 9, 131583\u2013131613 (2021). https:\/\/doi.org\/10.1109\/access.2021.3112996","journal-title":"IEEE Access"},{"issue":"1","key":"1283_CR153","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41598-022-10956-9","volume":"12","author":"YW Park","year":"2022","unstructured":"Park, Y.W., et al.: Cycle-consistent adversarial networks improves generalizability of radiomics model in grading meningiomas on external validation. Sci. Rep. 12(1), 1\u20139 (2022). https:\/\/doi.org\/10.1038\/s41598-022-10956-9","journal-title":"Sci. Rep."},{"key":"1283_CR154","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1142\/s2737599425500239","volume":"12","author":"L Liu","year":"2025","unstructured":"Liu, L., Hasikin, K., Jiang, Y., Xia, K., Lai, K.W.: BrainAdaptNet: a few-shot learning model for brain tumor segmentation in low-quality MRI. Innov. Emerg. Technol. 12, 1\u201311 (2025). https:\/\/doi.org\/10.1142\/s2737599425500239","journal-title":"Innov. Emerg. Technol."},{"issue":"3","key":"1283_CR155","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1002\/nbm.70001","volume":"38","author":"MA Islam","year":"2025","unstructured":"Islam, M.A., Mridha, M.F., Safran, M., Alfarhood, S., Kabir, M.M.: Revolutionizing brain tumor detection using explainable AI in MRI images. NMR Biomed. 38(3), 1\u201314 (2025). https:\/\/doi.org\/10.1002\/nbm.70001","journal-title":"NMR Biomed."},{"issue":"2","key":"1283_CR156","doi-asserted-by":"publisher","first-page":"313","DOI":"10.1148\/radiol.2021203786","volume":"299","author":"M Conte","year":"2021","unstructured":"Conte, M., et al.: Generative adversarial networks to synthesize missing T1 and FLAIR MRI sequences for use in a multisequence brain tumor segmentation model. Radiology 299(2), 313\u2013323 (2021). https:\/\/doi.org\/10.1148\/radiol.2021203786","journal-title":"Radiology"},{"key":"1283_CR157","doi-asserted-by":"publisher","unstructured":"Paproki, O. Salvado, and C. Fookes, \u201cSynthetic Data for Deep Learning in Computer Vision & Medical Imaging: A Means to Reduce Data Bias,\u201d ACM Computing Surveys, 56(11), 1\u201337 (2024). https:\/\/doi.org\/10.1145\/3663759","DOI":"10.1145\/3663759"},{"key":"1283_CR158","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2023.102789","volume":"86","author":"Billot","year":"2023","unstructured":"Billot, et al.: SynthSeg: Segmentation of brain MRI scans of any contrast and resolution without retraining. Med. Image Anal. 86, 102789 (Feb.2023). https:\/\/doi.org\/10.1016\/j.media.2023.102789","journal-title":"Med. Image Anal."},{"issue":"1","key":"1283_CR159","doi-asserted-by":"publisher","first-page":"1018","DOI":"10.1080\/19942060.2022.2056511","volume":"16","author":"U Haq","year":"2022","unstructured":"Haq, U., et al.: Computational modeling and simulation of stenosis of the cerebral aqueduct due to brain tumor. Eng. Appl. Comput. Fluid Mech. 16(1), 1018\u20131030 (2022). https:\/\/doi.org\/10.1080\/19942060.2022.2056511","journal-title":"Eng. Appl. Comput. Fluid Mech."},{"issue":"8","key":"1283_CR160","doi-asserted-by":"publisher","first-page":"4406","DOI":"10.1364\/boe.528535","volume":"15","author":"B Black","year":"2024","unstructured":"Black, B., Liquet, B., Di Ieva, A., Stummer, W., Molina, E.S.: Spectral library and method for sparse unmixing of hyperspectral images in fluorescence guided resection of brain tumors. Biomed. Opt. Express 15(8), 4406 (2024). https:\/\/doi.org\/10.1364\/boe.528535","journal-title":"Biomed. Opt. Express"},{"issue":"5","key":"1283_CR161","doi-asserted-by":"publisher","first-page":"1863","DOI":"10.1002\/mrm.29939","volume":"91","author":"S Fujita","year":"2024","unstructured":"Fujita, S., et al.: Cross\u2010vendor multiparametric mapping of the human brain using 3D\u2010QALAS: a multicenter and multivendor study. Magn. Reson. Med. 91(5), 1863\u20131875 (2024). https:\/\/doi.org\/10.1002\/mrm.29939","journal-title":"Magn. Reson. Med."},{"issue":"1","key":"1283_CR162","doi-asserted-by":"publisher","first-page":"27","DOI":"10.1097\/rli.0000000000001114","volume":"60","author":"Y Choi","year":"2024","unstructured":"Choi, Y., et al.: Beyond the conventional structural MRI. Invest. Radiol. 60(1), 27\u201342 (2024). https:\/\/doi.org\/10.1097\/rli.0000000000001114","journal-title":"Invest. Radiol."},{"key":"1283_CR163","doi-asserted-by":"publisher","unstructured":"M. Polsinelli, L. Cinque, F. Mignosi, G. Placidi, and G. Tortora: Siamese Network to Investigate Scanner-Dependency in MRI. IEEE CBMS\u201923 pp. 535\u2013541 (2023). https:\/\/doi.org\/10.1109\/cbms58004.2023.00275","DOI":"10.1109\/cbms58004.2023.00275"},{"issue":"1","key":"1283_CR164","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41598-023-43715-5","volume":"13","author":"R Kushol","year":"2023","unstructured":"Kushol, R., Parnianpour, P., Wilman, A.H., Kalra, S., Yang, Y.-H.: Effects of MRI scanner manufacturers in classification tasks with deep learning models. Sci. Rep. 13(1), 1\u201313 (2023). https:\/\/doi.org\/10.1038\/s41598-023-43715-5","journal-title":"Sci. Rep."},{"key":"1283_CR165","unstructured":"Staff, \u201cNavigating GDPR data sovereignty requirements,\u201d InCountry, May 31, 2024. https:\/\/incountry.com\/blog\/navigating-gdpr-data-sovereignty-requirements\/ (accessed Sep. 18, 2025)."},{"key":"1283_CR166","unstructured":"GDPR.eu, GDPR Archives - GDPR.eu, GDPR.eu (2025). https:\/\/gdpr.eu\/tag\/gdpr\/ (accessed 18 Sep 2025)"},{"issue":"2","key":"1283_CR167","doi-asserted-by":"publisher","first-page":"265","DOI":"10.1148\/radiol.211597","volume":"304","author":"CS Moskowitz","year":"2022","unstructured":"Moskowitz, C.S., Welch, M.L., Jacobs, M.A., Kurland, B.F., Simpson, A.L.: Radiomic analysis: study design, statistical analysis, and other bias mitigation strategies. Radiology 304(2), 265\u2013273 (2022). https:\/\/doi.org\/10.1148\/radiol.211597","journal-title":"Radiology"},{"key":"1283_CR168","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/access.2025.3555543","volume":"13","author":"S Amirian","year":"2025","unstructured":"Amirian, S., et al.: State-of-the-art in responsible, explainable, and fair AI for medical image analysis. IEEE Access 13, 1\u201335 (2025). https:\/\/doi.org\/10.1109\/access.2025.3555543","journal-title":"IEEE Access"},{"key":"1283_CR169","doi-asserted-by":"publisher","first-page":"251","DOI":"10.1016\/b978-0-443-45495-0.00015-2","volume-title":"Elsevier eBooks","author":"D Demetriou","year":"2025","unstructured":"Demetriou, D., et al.: Challenges and ethical considerations in artificial intelligence-driven brain cancer care. In: Elsevier eBooks, pp. 251\u2013268. (2025). https:\/\/doi.org\/10.1016\/b978-0-443-45495-0.00015-2"},{"key":"1283_CR170","unstructured":"ISO, IEC 62304:2006, Medical Device Software (2021). https:\/\/www.iso.org\/standard\/38421.html (accessed 18 Sep 2025)"},{"issue":"1","key":"1283_CR171","doi-asserted-by":"publisher","DOI":"10.1136\/bmjsit-2023-000186","volume":"5","author":"M Mooghali","year":"2023","unstructured":"Mooghali, M., Rathi, V.K., Kadakia, K.T., Ross, J.S., Dhruva, S.S.: Medical device risk (re)classification: lessons from the FDA\u2019s 515 Program Initiative. BMJ Surg. Interv. Health Technol. 5(1), e000186 (2023). https:\/\/doi.org\/10.1136\/bmjsit-2023-000186","journal-title":"BMJ Surg. Interv. Health Technol."},{"key":"1283_CR172","doi-asserted-by":"publisher","first-page":"94","DOI":"10.1109\/ojse.2024.3392691","volume":"2","author":"F Elkourdi","year":"2024","unstructured":"Elkourdi, F., Wei, C., Xiao, L., Yu, Z., Asan, O.: Exploring current practices and challenges of HIPAA compliance in software engineering: scoping review. IEEE Open J. Syst. Eng. 2, 94\u2013104 (2024). https:\/\/doi.org\/10.1109\/ojse.2024.3392691","journal-title":"IEEE Open J. Syst. Eng."},{"key":"1283_CR173","doi-asserted-by":"publisher","unstructured":"Peregrin:Managing HIPAA compliance includes legal and ethical considerations Journal of the Academy of Nutrition and Dietetics 121(2),327\u2013329 (2021). https:\/\/doi.org\/10.1016\/j.jand.2020.11.012","DOI":"10.1016\/j.jand.2020.11.012"},{"issue":"5","key":"1283_CR174","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0324285","volume":"20","author":"A Ahmed","year":"2025","unstructured":"Ahmed, A., Shahzad, A., Naseem, S., Ali, I., Ahmad, I.: Evaluating the effectiveness of data governance frameworks in ensuring security and privacy of healthcare data: A quantitative analysis of ISO standards, GDPR, and HIPAA in blockchain technology. PLoS ONE 20(5), e0324285 (2025). https:\/\/doi.org\/10.1371\/journal.pone.0324285","journal-title":"PLoS ONE"},{"issue":"1","key":"1283_CR175","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s13244-021-01081-8","volume":"12","author":"M Aiello","year":"2021","unstructured":"Aiello, M., Esposito, G., Pagliari, G., Borrelli, P., Brancato, V., Salvatore, M.: How does DICOM support big data management? Investigating its use in medical imaging community. Insights Imaging 12(1), 1\u201321 (2021). https:\/\/doi.org\/10.1186\/s13244-021-01081-8","journal-title":"Insights Imaging"},{"key":"1283_CR176","unstructured":"ISO, ISO 13485:2016,\u201d Medical Devices - Quality Management Systems - Requirements for Regulatory Purposes (2020). https:\/\/www.iso.org\/standard\/59752.html.(accessed 18 Sep 2025)."},{"key":"1283_CR177","unstructured":"ISO, ISO 14971:2019,\u201d Medical Devices - Application of Risk Management to Medical Devices (2025). https:\/\/www.iso.org\/standard\/72704.html.(accessed 18 Sep.\u00a0 2025)"},{"key":"1283_CR178","unstructured":"Office of the Commissioner Medical device de novo classification process U.S. Food And Drug Administration (2021). https:\/\/www.fda.gov\/about-fda\/economic-impact-analyses-fda-regulations\/medical-device-de-novo-classification-process.(accessed 18 Sep 2025)"},{"key":"1283_CR179","doi-asserted-by":"publisher","unstructured":"S. Shivshankar, N. Makhija and P. Mathusudhanan: Digital Imaging and Communication in Medicine (DICOM): Biomedical and Health Informatics: Imaging and interoperability using HL7 and DICOM in Advanced technologies and societal change pp. 299\u2013317 (2024). https:\/\/doi.org\/10.1007\/978-981-97-3312-5_20","DOI":"10.1007\/978-981-97-3312-5_20"},{"issue":"12","key":"1283_CR180","doi-asserted-by":"publisher","DOI":"10.3390\/healthcare11121729","volume":"11","author":"M Ayaz","year":"2023","unstructured":"Ayaz, M., Pasha, M.F., Alahmadi, T.J., Abdullah, N.N.B., Alkahtani, H.K.: Transforming healthcare analytics with FHIR: a framework for standardizing and analyzing clinical data. Healthcare 11(12), 1729 (2023). https:\/\/doi.org\/10.3390\/healthcare11121729","journal-title":"Healthcare"},{"key":"1283_CR181","doi-asserted-by":"publisher","DOI":"10.1016\/j.critrevonc.2025.104682","author":"Y Zhan","year":"2025","unstructured":"Zhan, Y., Hao, Y., Wang, X., Guo, D.: Advances of artificial intelligence in clinical application and scientific research of neuro-oncology: Current knowledge and future perspectives. Crit. Rev. Oncol. Hematol. (2025). https:\/\/doi.org\/10.1016\/j.critrevonc.2025.104682","journal-title":"Crit. Rev. Oncol. Hematol."},{"issue":"1","key":"1283_CR182","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s12911-024-02519-x","volume":"24","author":"MJZ Rahman","year":"2024","unstructured":"Rahman, M.J.Z., et al.: Advanced AI-driven approach for enhanced brain tumor detection from MRI images utilizing EfficientNetB2 with equalization and homomorphic filtering. BMC Med. Inform. Decis. Mak. 24(1), 1\u201319 (2024). https:\/\/doi.org\/10.1186\/s12911-024-02519-x","journal-title":"BMC Med. Inform. Decis. Mak."},{"key":"1283_CR183","doi-asserted-by":"publisher","DOI":"10.1109\/etecte63967.2024.10824029","author":"SU Rehman","year":"2024","unstructured":"Rehman, S.U., Anwer, S., Aftab, J., Hamza, A., Ahmed, A.: An Optimized Novel Hybrid CNN-Vision Transformer (ViT) Architecture for Brain Tumor Classification. IEEE ETECTE (2024). https:\/\/doi.org\/10.1109\/etecte63967.2024.10824029","journal-title":"IEEE ETECTE"},{"key":"1283_CR184","doi-asserted-by":"publisher","first-page":"1","DOI":"10.3389\/fonc.2025.1508451","volume":"15","author":"K Chandraprabha","year":"2025","unstructured":"Chandraprabha, K., Ganesan, L., Baskaran, K.: A novel approach for the detection of brain tumor and its classification via end-to-end Vision Transformer - CNN architecture. Front. Oncol. 15, 1\u201318 (2025). https:\/\/doi.org\/10.3389\/fonc.2025.1508451","journal-title":"Front. Oncol."},{"key":"1283_CR185","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2024.107220","volume":"102","author":"AC Yadav","year":"2024","unstructured":"Yadav, A.C., Kolekar, M.H., Zope, M.K.: Modified recurrent residual attention U-Net model for MRI-based brain tumor segmentation. Biomed. Signal Process. Control 102, 107220 (2024). https:\/\/doi.org\/10.1016\/j.bspc.2024.107220","journal-title":"Biomed. Signal Process. Control"},{"key":"1283_CR186","doi-asserted-by":"publisher","first-page":"152430","DOI":"10.1109\/access.2024.3480271","volume":"12","author":"AC Yadav","year":"2024","unstructured":"Yadav, A.C., Kolekar, M.H., Sonawane, Y., Kadam, G., Tiwarekar, S., Kalbande, D.R.: EFFUNET++: A novel architecture for brain tumor segmentation using FLAIR MRI images. IEEE Access 12, 152430\u2013152443 (2024). https:\/\/doi.org\/10.1109\/access.2024.3480271","journal-title":"IEEE Access"}],"container-title":["International Journal of Computational Intelligence Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s44196-026-01283-2","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s44196-026-01283-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s44196-026-01283-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,13]],"date-time":"2026-05-13T17:02:15Z","timestamp":1778691735000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s44196-026-01283-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3,26]]},"references-count":186,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2026,12]]}},"alternative-id":["1283"],"URL":"https:\/\/doi.org\/10.1007\/s44196-026-01283-2","relation":{},"ISSN":["1875-6883"],"issn-type":[{"value":"1875-6883","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,3,26]]},"assertion":[{"value":"1 November 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"6 March 2026","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"18 March 2026","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"26 March 2026","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing Interests"}},{"value":"All components of this paper were written & developed solely by the authors without the assistance of AI\u2019s.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Artificial Intelligence (AI) Disclosure Statement"}}],"article-number":"189"}}