{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T03:35:55Z","timestamp":1793504155557,"version":"4.5.0"},"reference-count":56,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,8,27]],"date-time":"2025-08-27T00:00:00Z","timestamp":1756252800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2025,8,27]],"date-time":"2025-08-27T00:00:00Z","timestamp":1756252800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"funder":[{"DOI":"10.13039\/501100004242","name":"Princess Nourah Bint Abdulrahman University","doi-asserted-by":"publisher","award":["PNURSP2025R512"],"award-info":[{"award-number":["PNURSP2025R512"]}],"id":[{"id":"10.13039\/501100004242","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Discov Onc"],"DOI":"10.1007\/s12672-025-03501-3","type":"journal-article","created":{"date-parts":[[2025,8,27]],"date-time":"2025-08-27T18:28:20Z","timestamp":1756319300000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":28,"title":["Improved brain tumor classification through DenseNet121 based transfer learning"],"prefix":"10.1007","volume":"16","author":[{"given":"Mehwish","family":"Rasheed","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Muhammad Arfan","family":"Jaffar","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Arslan","family":"Akram","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Javed","family":"Rashid","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tagrid Abdullah N.","family":"Alshalali","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0594-5877","authenticated-orcid":false,"given":"Asma","family":"Irshad","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8681-6382","authenticated-orcid":false,"given":"Nadeem","family":"Sarwar","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,8,27]]},"reference":[{"issue":"3","key":"3501_CR1","doi-asserted-by":"publisher","first-page":"E168","DOI":"10.1093\/neuros\/nyy543","volume":"84","author":"JJ Graber","year":"2019","unstructured":"Graber JJ, Cobbs CS, Olson JJ. Congress of neurological surgeons systematic review and evidence-based guidelines on the use of stereotactic radiosurgery in the treatment of adults with metastatic brain tumors. Neurosurgery. 2019;84(3):E168\u201370.","journal-title":"Neurosurgery"},{"key":"3501_CR2","doi-asserted-by":"publisher","first-page":"34","DOI":"10.1016\/j.compmedimag.2019.05.001","volume":"75","author":"ZNK Swati","year":"2019","unstructured":"Swati ZNK, et al. Brain tumor classification for MR images using transfer learning and fine-tuning. Comput Med Imaging Graph. 2019;75:34\u201346.","journal-title":"Comput Med Imaging Graph"},{"issue":"4","key":"3501_CR3","doi-asserted-by":"publisher","first-page":"753","DOI":"10.1007\/s11760-020-01793-2","volume":"15","author":"JD Bodapati","year":"2021","unstructured":"Bodapati JD, Shaik NS, Naralasetti V, Mundukur NB. Joint training of two-channel deep neural network for brain tumor classification. SIViP. 2021;15(4):753\u201360.","journal-title":"SIViP"},{"issue":"4","key":"3501_CR4","doi-asserted-by":"publisher","first-page":"778","DOI":"10.1002\/ijc.33588","volume":"149","author":"J Ferlay","year":"2021","unstructured":"Ferlay J, et al. Cancer statistics for the year 2020: an overview. Int J Cancer. 2021;149(4):778\u201389.","journal-title":"Int J Cancer"},{"issue":"1","key":"3501_CR5","doi-asserted-by":"publisher","first-page":"1235","DOI":"10.32604\/cmc.2023.032005","volume":"74","author":"J Rashid","year":"2023","unstructured":"Rashid J, Khan I, Ali G, Rehman SU, Alturise F. Real-time multiple guava leaf disease detection from a single leaf using hybrid deep learning technique. Computers Materials Continua. 2023;74(1):1235\u201357.","journal-title":"Computers Materials Continua"},{"key":"3501_CR6","doi-asserted-by":"publisher","DOI":"10.1007\/s11042-023-16776-x","author":"J Rashid","year":"2023","unstructured":"Rashid J, Qaisar BS, Faheem M, Akram A, Amin Rul, Hamid M. Mouth and oral disease classification using InceptionResNetV2 method. Multimed Tools Appl Sep. 2023. https:\/\/doi.org\/10.1007\/s11042-023-16776-x.","journal-title":"Multimed Tools Appl Sep"},{"issue":"1","key":"3501_CR7","doi-asserted-by":"publisher","first-page":"145","DOI":"10.32604\/cmc.2023.041074","volume":"78","author":"A Akram","year":"2024","unstructured":"Akram A, Rashid J, Jaffar A, Hajjej F, Iqbal W, Sarwar N. Weber law based approach for Multi-Class image forgery detection. CMC. 2024;78(1):145\u201366. https:\/\/doi.org\/10.32604\/cmc.2023.041074.","journal-title":"CMC"},{"issue":"1","key":"3501_CR8","doi-asserted-by":"publisher","first-page":"1311","DOI":"10.32604\/cmc.2023.040512","volume":"78","author":"A Akram","year":"2024","unstructured":"Akram A, et al. Enhanced steganalysis for color images using curvelet features and support vector machine. CMC. 2024;78(1):1311\u201328. https:\/\/doi.org\/10.32604\/cmc.2023.040512.","journal-title":"CMC"},{"issue":"20","key":"3501_CR9","first-page":"e17","volume":"5","author":"R Meera","year":"2018","unstructured":"Meera R, Anandhan P. A review on automatic detection of brain tumor using computer aided diagnosis system through MRI. EAI Endorsed Trans Energy Web. 2018;5(20):e17\u201317.","journal-title":"EAI Endorsed Trans Energy Web"},{"key":"3501_CR10","doi-asserted-by":"publisher","unstructured":"Tripathy BK, Parikh S, Ajay P, Magapu C. 10 - Brain MRI segmentation techniques based on CNN and its variants. In: Chaki J, editor. in Brain tumor MRI image segmentation using deep learning techniques. Academic; 2022. pp. 161\u201383. https:\/\/doi.org\/10.1016\/B978-0-323-91171-9.00001-6.","DOI":"10.1016\/B978-0-323-91171-9.00001-6"},{"key":"3501_CR11","doi-asserted-by":"publisher","first-page":"64758","DOI":"10.1109\/ACCESS.2023.3288017","volume":"11","author":"S Rajendran","year":"2023","unstructured":"Rajendran S, et al. Automated segmentation of brain tumor MRI images using deep learning. IEEE Access. 2023;11:64758\u201368. https:\/\/doi.org\/10.1109\/ACCESS.2023.3288017.","journal-title":"IEEE Access"},{"key":"3501_CR12","doi-asserted-by":"publisher","unstructured":"Khairandish MO, Sharma M, Jain V, Chatterjee JM, Jhanjhi NZ. A hybrid CNN-SVM threshold segmentation approach for tumor detection and classification of MRI brain images. IRBM. 2022. 43(4):290\u20139. https:\/\/doi.org\/10.1016\/j.irbm.2021.06.003.","DOI":"10.1016\/j.irbm.2021.06.003"},{"issue":"1","key":"3501_CR13","doi-asserted-by":"publisher","first-page":"p8104054","DOI":"10.1155\/2022\/8104054","volume":"2022","author":"AH Khan","year":"2022","unstructured":"Khan AH, et al. Intelligent model for brain tumor identification using deep learning. Appl Comput Intell Soft Comput. 2022;2022(1):p8104054. https:\/\/doi.org\/10.1155\/2022\/8104054.","journal-title":"Appl Comput Intell Soft Comput"},{"key":"3501_CR14","unstructured":"Parida A, et al. Adult glioma segmentation in Sub-Saharan Africa using transfer learning on stratified finetuning data, arXiv.org. https:\/\/arxiv.org\/abs\/2412.04111v2. Accessed 19 June 2025."},{"key":"3501_CR15","doi-asserted-by":"publisher","unstructured":"Mart\u00edn-P\u00e9rez A, et al. Unifying heterogeneous hyperspectral databases for in vivo human brain cancer classification: Towards robust algorithm development. Computer Methods Programs Biomed Update. 2025;7:100183. https:\/\/doi.org\/10.1016\/j.cmpbup.2025.100183.","DOI":"10.1016\/j.cmpbup.2025.100183"},{"key":"3501_CR16","doi-asserted-by":"publisher","unstructured":"Ishaq A, Ullah FUM, Hamandawana P, Cho D-J, Chung T-S. Improved efficientnet architecture for multi-grade brain tumor detection. Electronics. 2025;14(4):4. https:\/\/doi.org\/10.3390\/electronics14040710.","DOI":"10.3390\/electronics14040710"},{"key":"3501_CR17","doi-asserted-by":"publisher","unstructured":"Khaniki MAL, Mirzaeibonehkhater M, Manthouri M, Hasani E. Vision transformer with feature calibration and selective cross-attention for brain tumor classification. Iran J Comput Sci. 2025;8(2):335\u201347. https:\/\/doi.org\/10.1007\/s42044-024-00220-w.","DOI":"10.1007\/s42044-024-00220-w"},{"key":"3501_CR18","unstructured":"Sapra P, Singh R, Khurana S. Brain tumor detection using neural network. Int J Sci Mod Eng (IJISME). 2013;2319\u20136386."},{"key":"3501_CR19","doi-asserted-by":"crossref","unstructured":"Sharma K, Kaur A, Gujral S. Brain tumor detection based on machine learning algorithms. Int J Computer Appl. 2014;103(1). https:\/\/www.academia.edu\/download\/69364750\/pxc3896883.pdf. Accessed 11 Feb 2025.","DOI":"10.5120\/18036-6883"},{"key":"3501_CR20","doi-asserted-by":"publisher","unstructured":"Kumar TS, Rashmi K, Ramadoss S, Sandhya LK, Sangeetha TJ. Brain tumor detection using SVM classifier. In: 2017 Third International Conference on Sensing, Signal Processing and Security (ICSSS). 2017. pp. 318\u201323. https:\/\/doi.org\/10.1109\/SSPS.2017.8071613.","DOI":"10.1109\/SSPS.2017.8071613"},{"key":"3501_CR21","doi-asserted-by":"publisher","first-page":"258","DOI":"10.1007\/978-3-030-49339-4_26","volume-title":"Innovations in Bio-Inspired computing and applications","author":"A Upadhyay","year":"2021","unstructured":"Upadhyay A, Palival U, Jaiswal S. Early brain tumor detection using random forest classification. In: Abraham A, Panda M, Pradhan S, Garcia-Hernandez L, Ma K, editors. in Innovations in Bio-Inspired computing and applications. Cham: Springer International Publishing; 2021. pp. 258\u201364. https:\/\/doi.org\/10.1007\/978-3-030-49339-4_26."},{"issue":"6","key":"3501_CR22","first-page":"87","volume":"14","author":"J Naik","year":"2014","unstructured":"Naik J, Patel S. Tumor detection and classification using decision tree in brain MRI. Int J Comput Sci Netw Secur (ijcsns). 2014;14(6):87.","journal-title":"Int J Comput Sci Netw Secur (ijcsns)"},{"issue":"1","key":"3501_CR23","doi-asserted-by":"publisher","first-page":"16","DOI":"10.1186\/s12911-023-02114-6","volume":"23","author":"S Saeedi","year":"2023","unstructured":"Saeedi S, Rezayi S, Keshavarz H, Niakan Kalhori SR. MRI-based brain tumor detection using convolutional deep learning methods and chosen machine learning techniques. BMC Med Inf Decis Mak. 2023;23(1):16.","journal-title":"BMC Med Inf Decis Mak"},{"key":"3501_CR24","doi-asserted-by":"publisher","first-page":"1296","DOI":"10.1002\/jemt.23688","volume":"84","author":"T Sadad","year":"2021","unstructured":"Sadad T, et al. Brain tumor detection and multi-classification using advanced deep learning techniques. Microsc Res Tech. 2021;84:1296\u2013308. https:\/\/doi.org\/10.1002\/jemt.23688.","journal-title":"Microsc Res Tech"},{"key":"3501_CR25","unstructured":"Qin X, Wang Z. Nasnet: A neuron attention stage-by-stage net for single image deraining. arXiv preprint arXiv:1912.03151. 2019. https:\/\/www.researchgate.net\/profile\/Xu-Qin-12\/publication\/337830328_NASNet_A_Neuron_Attention_Stage-by-Stage_Net_for_Single_Image_Deraining\/links\/5e193ee3299bf10bc3a34e94\/NASNet-A-Neuron-Attention-Stage-by-Stage-Net-for-Single-Image-Deraining.pdf. Accessed 11 Feb 2025."},{"key":"3501_CR26","doi-asserted-by":"publisher","unstructured":"Saba T, Sameh Mohamed A, El-Affendi M, Amin J, Sharif M. Brain tumor detection using fusion of hand crafted and deep learning features. Cogn Syst Res. 2020;59:221\u201330. https:\/\/doi.org\/10.1016\/j.cogsys.2019.09.007.","DOI":"10.1016\/j.cogsys.2019.09.007"},{"key":"3501_CR27","doi-asserted-by":"publisher","unstructured":"Amin J, Sharif M, Raza M, Saba T, Sial R, Shad SA. Brain tumor detection: a long short-term memory (LSTM)-based learning model. Neural Comput Appl. 2020;32(20):15965\u201373. https:\/\/doi.org\/10.1007\/s00521-019-04650-7.","DOI":"10.1007\/s00521-019-04650-7"},{"key":"3501_CR28","doi-asserted-by":"publisher","unstructured":"Sharif MI, Li JP, Khan MA, Saleem MA. Active deep neural network features selection for segmentation and recognition of brain tumors using MRI images. Pattern Recognit Lett. 2020;129:181\u20139. https:\/\/doi.org\/10.1016\/j.patrec.2019.11.019.","DOI":"10.1016\/j.patrec.2019.11.019"},{"key":"3501_CR29","doi-asserted-by":"publisher","unstructured":"Nazir M, Shakil S, Khurshid K. Role of deep learning in brain tumor detection and classification (2015 to 2020): a review. Comput Med Imaging Graph. 2021;91:101940. https:\/\/doi.org\/10.1016\/j.compmedimag.2021.101940.","DOI":"10.1016\/j.compmedimag.2021.101940"},{"key":"3501_CR30","doi-asserted-by":"publisher","unstructured":"Sajid S, Hussain S, Sarwar A. Brain tumor detection and segmentation in MR images using deep learning. Arab J Sci Eng. 2019;44(11):9249\u201361. https:\/\/doi.org\/10.1007\/s13369-019-03967-8.","DOI":"10.1007\/s13369-019-03967-8"},{"issue":"8","key":"3501_CR31","doi-asserted-by":"publisher","first-page":"1451","DOI":"10.3390\/diagnostics13081451","volume":"13","author":"M Rasheed","year":"2023","unstructured":"Rasheed M, et al. Recognizing brain tumors using adaptive noise filtering and statistical features. Diagnostics. 2023;13(8):1451.","journal-title":"Diagnostics"},{"key":"3501_CR32","doi-asserted-by":"publisher","unstructured":"Cheng J, et al. Enhanced performance of brain tumor classification via tumor region augmentation and partition. PLoS ONE. 2015;10(10):e0140381. https:\/\/doi.org\/10.1371\/journal.pone.0140381.","DOI":"10.1371\/journal.pone.0140381"},{"key":"3501_CR33","doi-asserted-by":"publisher","unstructured":"Babu Vimala B, Srinivasan S, Mathivanan SK, Mahalakshmi P, Jayagopal, Dalu GT. Detection and classification of brain tumor using hybrid deep learning models. Sci Rep. 2023;13(1):23029. https:\/\/doi.org\/10.1038\/s41598-023-50505-6.","DOI":"10.1038\/s41598-023-50505-6"},{"key":"3501_CR34","doi-asserted-by":"publisher","unstructured":"Zahid U, et al. BrainNet: optimal deep learning feature fusion for brain tumor classification. Comput Intell Neurosci. 2022. 2022(1):1465173. https:\/\/doi.org\/10.1155\/2022\/1465173.","DOI":"10.1155\/2022\/1465173"},{"key":"3501_CR35","doi-asserted-by":"crossref","unstructured":"Hapsari PAT, et al. Brain tumor classification in MRI images using En-CNN. Int J Intell Eng Syst. 2021;14(4):4.","DOI":"10.22266\/ijies2021.0831.38"},{"key":"3501_CR36","doi-asserted-by":"publisher","unstructured":"De Souza DL, Coelho AM, Felipe JC, Baffa MDFO. Brain tumor classification on multi-modality MRI using radiomic features. In: 2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). 2021. pp. 1799\u2013804. https:\/\/doi.org\/10.1109\/BIBM52615.2021.9669791.","DOI":"10.1109\/BIBM52615.2021.9669791"},{"key":"3501_CR37","doi-asserted-by":"publisher","unstructured":"Gupta T, Gandhi TK, Gupta RK, Panigrahi BK. Classification of patients with tumor using MR FLAIR images. Pattern Recognit Lett. 2020;139:112\u20137. https:\/\/doi.org\/10.1016\/j.patrec.2017.10.037.","DOI":"10.1016\/j.patrec.2017.10.037"},{"key":"3501_CR38","doi-asserted-by":"publisher","unstructured":"Rajasree R, Columbus CC, Shilaja C. Multiscale-based multimodal image classification of brain tumor using deep learning method. Neural Comput & Appl. 2021. 33(11):5543\u201353. https:\/\/doi.org\/10.1007\/s00521-020-05332-5.","DOI":"10.1007\/s00521-020-05332-5"},{"key":"3501_CR39","doi-asserted-by":"publisher","unstructured":"Sharma S, et al. [Retracted] deep learning model for automatic classification and prediction of brain tumor. J Sens. 2022;2022(1):3065656. https:\/\/doi.org\/10.1155\/2022\/3065656.","DOI":"10.1155\/2022\/3065656"},{"key":"3501_CR40","doi-asserted-by":"publisher","unstructured":"Guo S, Wang L, Chen Q, Wang L, Zhang J, Zhu Y. Multimodal MRI image decision fusion-based network for glioma classification. Front Oncol. 2022;12. https:\/\/doi.org\/10.3389\/fonc.2022.819673.","DOI":"10.3389\/fonc.2022.819673"},{"key":"3501_CR41","doi-asserted-by":"publisher","unstructured":"Shinde AS, Mahendra B, Nejakar S, Herur SM, Bhat N. Performance analysis of machine learning algorithm of detection and classification of brain tumor using computer vision. Adv Eng Softw. 2022;173:103221. https:\/\/doi.org\/10.1016\/j.advengsoft.2022.103221.","DOI":"10.1016\/j.advengsoft.2022.103221"},{"issue":"1","key":"3501_CR42","first-page":"32","volume":"11","author":"OA Kamil","year":"2021","unstructured":"Kamil OA, Al-Shammari SW. IoT framework for brain tumor classification using optimized CNN-MRFO model. Am J Bioinf Res. 2021;11(1):32\u20137.","journal-title":"Am J Bioinf Res"},{"issue":"3","key":"3501_CR43","doi-asserted-by":"publisher","first-page":"1026","DOI":"10.1111\/1556-4029.70033","volume":"70","author":"A Akram","year":"2025","unstructured":"Akram A, Jaffar MA, Rashid J, Boulaaras SM, Faheem M. CMV2U-Net: A U-shaped network with edge-weighted features for detecting and localizing image splicing. J Forensic Sci. 2025;70(3):1026\u201343. https:\/\/doi.org\/10.1111\/1556-4029.70033.","journal-title":"J Forensic Sci"},{"key":"3501_CR44","doi-asserted-by":"publisher","unstructured":"Akram A, Ramzan S, Rasool A, Jaffar A, Furqan U, Javed W. Image splicing detection using discriminative robust local binary pattern and support vector machine. World J Eng. 2022. 19(4):459\u201366. https:\/\/doi.org\/10.1108\/WJE-09-2020-0456.","DOI":"10.1108\/WJE-09-2020-0456"},{"issue":"11","key":"3501_CR45","doi-asserted-by":"publisher","first-page":"e13524","DOI":"10.1111\/srt.13524","volume":"29","author":"A Akram","year":"2023","unstructured":"Akram A, Rashid J, Jaffar MA, Faheem M, Amin Rul. Segmentation and classification of skin lesions using hybrid deep learning method in the internet of medical things. Skin Res Technol. 2023;29(11):e13524. https:\/\/doi.org\/10.1111\/srt.13524.","journal-title":"Skin Res Technol"},{"key":"3501_CR46","doi-asserted-by":"crossref","unstructured":"Akram A, Tariq A, Ali MS, Tariq MU, Altaf A. Recognizing facial expressions across cultures using gradient features. J Innov Comput Emerg Technol. 2023;3(1).","DOI":"10.56536\/jicet.v3i1.54"},{"issue":"1","key":"3501_CR47","doi-asserted-by":"publisher","first-page":"1081","DOI":"10.32604\/cmc.2023.041558","volume":"77","author":"A Akram","year":"2023","unstructured":"Akram A, et al. Recognizing breast cancer using Edge-Weighted texture features of histopathology images. CMC. 2023;77(1):1081\u2013101. https:\/\/doi.org\/10.32604\/cmc.2023.041558.","journal-title":"CMC"},{"key":"3501_CR48","doi-asserted-by":"crossref","unstructured":"Filatov D, Yar GNAH. Brain tumor diagnosis and classification via pre-trained convolutional neural networks. arXiv preprint arXiv:2208.00768. 2022.","DOI":"10.1101\/2022.07.18.22277779"},{"issue":"4","key":"3501_CR49","doi-asserted-by":"publisher","first-page":"955","DOI":"10.3390\/electronics12040955","volume":"12","author":"MA G\u00f3mez-Guzm\u00e1n","year":"2023","unstructured":"G\u00f3mez-Guzm\u00e1n MA, et al. Classifying brain tumors on magnetic resonance imaging by using convolutional neural networks. Electronics. 2023;12(4):955.","journal-title":"Electronics"},{"key":"3501_CR50","doi-asserted-by":"crossref","unstructured":"Rajkumar S, Karthick K, Selvanathan N, Saravanan UB, Murali M, Dhiyanesh B. Brain tumor detection using deep learning neural network for medical internet of things applications. In: Presented at the 2021 6th International Conference on Communication and Electronics Systems (ICCES). IEEE;2021. pp. 1\u20138.","DOI":"10.1109\/ICCES51350.2021.9489187"},{"key":"3501_CR51","doi-asserted-by":"publisher","unstructured":"Mohanty BC, Subudhi PK, Dash R, Mohanty B. Feature-enhanced deep learning technique with soft attention for MRI-based brain tumor classification. Int J Inf Tecnol. 2024. 16(3):1617\u201326. https:\/\/doi.org\/10.1007\/s41870-023-01701-0.","DOI":"10.1007\/s41870-023-01701-0"},{"key":"3501_CR52","doi-asserted-by":"publisher","unstructured":"Dikande Simo AM, Tchagna Kouanou A, Monthe V, Kameni Nana M, Moffo Lonla B. Introducing a deep learning method for brain tumor classification using MRI data towards better performance. Inf Med Unlocked. 2024;44:101423. https:\/\/doi.org\/10.1016\/j.imu.2023.101423.","DOI":"10.1016\/j.imu.2023.101423"},{"key":"3501_CR53","doi-asserted-by":"publisher","unstructured":"Dutta TK, Nayak DR, Zhang Y-D. Attention-guided residual multiscale CNN for multiclass brain tumor classification using MR images. Biomed Signal Process Control. 2024;87:105421. https:\/\/doi.org\/10.1016\/j.bspc.2023.105421.","DOI":"10.1016\/j.bspc.2023.105421"},{"key":"3501_CR54","doi-asserted-by":"publisher","unstructured":"Amin M, et al. DieT transformer model with PCA-ADE integration for advanced multi-class brain tumor classification. Intelligence-Based Med. 2025;11:100192. https:\/\/doi.org\/10.1016\/j.ibmed.2024.100192.","DOI":"10.1016\/j.ibmed.2024.100192"},{"key":"3501_CR55","doi-asserted-by":"publisher","unstructured":"Hekmat A, Zhang Z, Rehman Khan SU, Bilal O. Brain tumor diagnosis redefined: leveraging image fusion for MRI enhancement classification. Biomed Signal Process Control. 2025;109:108040. https:\/\/doi.org\/10.1016\/j.bspc.2025.108040.","DOI":"10.1016\/j.bspc.2025.108040"},{"key":"3501_CR56","doi-asserted-by":"publisher","unstructured":"Guder O, Cetin-Kaya Y. Optimized attention-based lightweight CNN using particle swarm optimization for brain tumor classification. Biomed Signal Process Control. 2025;100:107126. https:\/\/doi.org\/10.1016\/j.bspc.2024.107126.","DOI":"10.1016\/j.bspc.2024.107126"}],"container-title":["Discover Oncology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12672-025-03501-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s12672-025-03501-3\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12672-025-03501-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,10]],"date-time":"2025-09-10T04:51:40Z","timestamp":1757479900000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s12672-025-03501-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,8,27]]},"references-count":56,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["3501"],"URL":"https:\/\/doi.org\/10.1007\/s12672-025-03501-3","relation":{},"ISSN":["2730-6011"],"issn-type":[{"value":"2730-6011","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,8,27]]},"assertion":[{"value":"2 May 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"25 August 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 August 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Not applicable.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"1645"}}