{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,3]],"date-time":"2026-04-03T07:50:41Z","timestamp":1775202641991,"version":"3.50.1"},"reference-count":81,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T00:00:00Z","timestamp":1743033600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T00:00:00Z","timestamp":1743033600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Discov Artif Intell"],"DOI":"10.1007\/s44163-025-00247-3","type":"journal-article","created":{"date-parts":[[2025,3,30]],"date-time":"2025-03-30T13:53:37Z","timestamp":1743342817000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["From black box AI to XAI in neuro-oncology: a survey on MRI-based tumor detection"],"prefix":"10.1007","volume":"5","author":[{"family":"Asmita","sequence":"first","affiliation":[]},{"given":"Praveen","family":"Mittal","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,3,27]]},"reference":[{"key":"247_CR1","doi-asserted-by":"publisher","first-page":"803","DOI":"10.1007\/s00401-016-1545-1","volume":"131","author":"DN Louis","year":"2016","unstructured":"Louis DN, Perry A, Reifenberger G, Von Deimling A, Figarella-Branger D, Cavenee WK, Ohgaki H, Wiestler OD, Kleihues P, Ellison DW. The 2016 world health organization classification of tumors of the central nervous system: a summary. Acta Neuropathol. 2016;131:803\u201320.","journal-title":"Acta Neuropathol"},{"key":"247_CR2","doi-asserted-by":"publisher","first-page":"181","DOI":"10.1016\/j.patrec.2019.11.019","volume":"129","author":"MI Sharif","year":"2020","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 Recogn Lett. 2020;129:181\u20139.","journal-title":"Pattern Recogn Lett"},{"issue":"3","key":"247_CR3","doi-asserted-by":"publisher","first-page":"168","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):168\u201370.","journal-title":"Neurosurgery"},{"key":"247_CR4","doi-asserted-by":"publisher","first-page":"197969","DOI":"10.1109\/ACCESS.2020.3034217","volume":"8","author":"MA Khan","year":"2020","unstructured":"Khan MA, Sarfraz MS, Alhaisoni M, Albesher AA, Wang S, Ashraf I. Stomachnet: optimal deep learning features fusion for stomach abnormalities classification. IEEE Access. 2020;8:197969\u201381.","journal-title":"IEEE Access"},{"key":"247_CR5","doi-asserted-by":"publisher","first-page":"293","DOI":"10.1016\/j.patrec.2019.11.034","volume":"129","author":"MA Khan","year":"2020","unstructured":"Khan MA, Sharif M, Akram T, Bukhari SAC, Nayak RS. Developed newton-raphson based deep features selection framework for skin lesion recognition. Pattern Recogn Lett. 2020;129:293\u2013303.","journal-title":"Pattern Recogn Lett"},{"key":"247_CR6","doi-asserted-by":"publisher","DOI":"10.1136\/bmj.n71","author":"MJ Page","year":"2020","unstructured":"Page MJ, McKenzie JE, Bossuyt PM, Boutron I, Hoffmann TC, Mulrow CD, Shamseer L, Tetzlaff JM, Akl EA, Brennan SE, et al. The prisma 2020 statement: an updated guideline for reporting systematic reviews. BMJ. 2020. https:\/\/doi.org\/10.1136\/bmj.n71.","journal-title":"BMJ"},{"key":"247_CR7","volume-title":"The brain: an introduction to functional neuroanatomy","author":"C Watson","year":"2010","unstructured":"Watson C, Kirkcaldie M, Paxinos G. The brain: an introduction to functional neuroanatomy. Cambridge: Academic Press; 2010."},{"key":"247_CR8","unstructured":"Dubin M. Experiencing flow at work as a digital native in an accelerated knowledge economy. PhD thesis, The Claremont Graduate University. 2018."},{"issue":"7","key":"247_CR9","doi-asserted-by":"publisher","first-page":"2164","DOI":"10.1148\/rg.2017170037","volume":"37","author":"DR Johnson","year":"2017","unstructured":"Johnson DR, Guerin JB, Giannini C, Morris JM, Eckel LJ, Kaufmann TJ. 2016 updates to the who brain tumor classification system: what the radiologist needs to know. Radiographics. 2017;37(7):2164\u201380.","journal-title":"Radiographics"},{"key":"247_CR10","doi-asserted-by":"publisher","DOI":"10.11113\/jt.v74.4670","author":"NM Saad","year":"2015","unstructured":"Saad NM, Bakar SARSA, Muda AS, Mokji MM. Review of brain lesion detection and classification using neuroimaging analysis techniques. J Teknol. 2015. https:\/\/doi.org\/10.11113\/jt.v74.4670.","journal-title":"J Teknol"},{"issue":"1","key":"247_CR11","doi-asserted-by":"publisher","first-page":"115","DOI":"10.1016\/j.nec.2004.07.007","volume":"16","author":"LCH Cruz","year":"2005","unstructured":"Cruz LCH, Sorensen AG. Diffusion tensor magnetic resonance imaging of brain tumors. Neurosurg Clin. 2005;16(1):115\u201334.","journal-title":"Neurosurg Clin"},{"key":"247_CR12","doi-asserted-by":"crossref","unstructured":"Rohith R, Dayalan MJ, Meena M, Varalakshmi P. Exploring deep learning techniques for MRI brain tumor image segmentation: a survey. In: 2024 International Conference on Advances in Computing, Communication and Applied Informatics (ACCAI). IEEE. 2024;1\u20135","DOI":"10.1109\/ACCAI61061.2024.10601935"},{"key":"247_CR13","doi-asserted-by":"publisher","DOI":"10.1007\/s10278-024-01283-8","author":"S Bouhafra","year":"2024","unstructured":"Bouhafra S, El Bahi H. Deep learning approaches for brain tumor detection and classification using MRI images (2020 to 2024): a systematic review. J Imag Inform Med. 2024. https:\/\/doi.org\/10.1007\/s10278-024-01283-8.","journal-title":"J Imag Inform Med"},{"key":"247_CR14","doi-asserted-by":"publisher","first-page":"83","DOI":"10.1201\/9781003320340-7","volume-title":"Research advances in intelligent computing","author":"H Ramamoorthy","year":"2023","unstructured":"Ramamoorthy H, Ramasundaram M, Pitchai A. A comprehensive exploration of brain tumor segmentation using deep learning techniques. In: Research advances in intelligent computing. Boca Raton: CRC Press; 2023. p. 83\u2013100."},{"key":"247_CR15","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.1911.02265","author":"P Saxena","year":"2019","unstructured":"Saxena P, Maheshwari A, Maheshwari S. Predictive modeling of brain tumor: a deep learning approach. ArXiv. 2019. https:\/\/doi.org\/10.48550\/arXiv.1911.02265.","journal-title":"ArXiv"},{"issue":"3","key":"247_CR16","doi-asserted-by":"publisher","first-page":"273","DOI":"10.1007\/BF00994018","volume":"20","author":"C Cortes","year":"1995","unstructured":"Cortes C, Vapnik V. Support-vector networks. Mach Learn. 1995;20(3):273\u201397. https:\/\/doi.org\/10.1007\/BF00994018.","journal-title":"Mach Learn"},{"key":"247_CR17","volume-title":"Statistical learning theory","author":"VN Vapnik","year":"1998","unstructured":"Vapnik VN. Statistical learning theory. New York: Wiley; 1998."},{"key":"247_CR18","doi-asserted-by":"publisher","first-page":"77","DOI":"10.1613\/jair.279","volume":"4","author":"JR Quinlan","year":"1996","unstructured":"Quinlan JR. Improved use of continuous attributes in c4.5. J Artif Intell Res. 1996;4:77\u201390. https:\/\/doi.org\/10.1613\/jair.279.","journal-title":"J Artif Intell Res"},{"issue":"1","key":"247_CR19","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1023\/A:1010933404324","volume":"45","author":"L Breiman","year":"2001","unstructured":"Breiman L. Random forests. Mach Learn. 2001;45(1):5\u201332. https:\/\/doi.org\/10.1023\/A:1010933404324.","journal-title":"Mach Learn"},{"issue":"1","key":"247_CR20","doi-asserted-by":"publisher","first-page":"21","DOI":"10.1109\/TIT.1967.1053964","volume":"13","author":"TM Cover","year":"1967","unstructured":"Cover TM, Hart PE. Nearest neighbor pattern classification. IEEE Trans Inform Theor. 1967;13(1):21\u20137. https:\/\/doi.org\/10.1109\/TIT.1967.1053964.","journal-title":"IEEE Trans Inform Theor"},{"issue":"8s","key":"247_CR21","first-page":"01","volume":"12","author":"P Mehta","year":"2023","unstructured":"Mehta P, Narwadkar S, Chillarge G, Rathi S, Shinde G, Shewale C. A new approach to brain tumor detection with CNNS: addressing the issues of standardization and generalizability. Int J Intell Syst Appl Eng. 2023;12(8s):01\u201313.","journal-title":"Int J Intell Syst Appl Eng"},{"issue":"4","key":"247_CR22","doi-asserted-by":"publisher","first-page":"290","DOI":"10.1016\/j.irbm.2021.06.003","volume":"43","author":"MO Khairandish","year":"2022","unstructured":"Khairandish MO, Sharma M, Jain V, Chatterjee JM, Jhanjhi N. A hybrid CNN-SVM threshold segmentation approach for tumor detection and classification of MRI brain images. IRBM. 2022;43(4):290\u20139.","journal-title":"IRBM"},{"key":"247_CR23","doi-asserted-by":"crossref","unstructured":"Shahajad M, Gambhir D, Gandhi R. Features extraction for classification of brain tumor MRI images using support vector machine. In: 2021 11th International Conference on Cloud Computing, Data Science & Engineering (Confluence). IEEE. 2021;767\u2013772.","DOI":"10.1109\/Confluence51648.2021.9377111"},{"key":"247_CR24","volume-title":"Brain tumor identification and classification of MRI images using deep learning techniques","author":"Z Jia","year":"2020","unstructured":"Jia Z, Chen D. Brain tumor identification and classification of MRI images using deep learning techniques. IEEE Access: Piscataway; 2020."},{"key":"247_CR25","doi-asserted-by":"crossref","unstructured":"Ramdlon RH, Kusumaningtyas EM, Karlita T. Brain tumor classification using MRI images with k-nearest neighbor method. In: 2019 International Electronics Symposium (IES). IEEE. 2019; 660\u2013667.","DOI":"10.1109\/ELECSYM.2019.8901560"},{"key":"247_CR26","doi-asserted-by":"crossref","unstructured":"Gurbin\u02d8a M, Lascu M, Lascu D. Tumor detection and classification of mri brain image using different wavelet transforms and support vector machines. In: 2019 42nd International Conference on Telecommunications and Signal Processing (TSP). IEEE. 2019;505\u2013508.","DOI":"10.1109\/TSP.2019.8769040"},{"issue":"3","key":"247_CR27","doi-asserted-by":"publisher","first-page":"353","DOI":"10.1002\/ima.22331","volume":"29","author":"R Rajagopal","year":"2019","unstructured":"Rajagopal R. Glioma brain tumor detection and segmentation using weighting random forest classifier with optimized ant colony features. Int J Imaging Syst Technol. 2019;29(3):353\u20139.","journal-title":"Int J Imaging Syst Technol"},{"issue":"12","key":"247_CR28","first-page":"187","volume":"10","author":"A Aiwale","year":"2019","unstructured":"Aiwale A, Ansari S. Brain tumor detection using KNN. Int J Sci Eng Res. 2019;10(12):187\u201393.","journal-title":"Int J Sci Eng Res"},{"key":"247_CR29","doi-asserted-by":"crossref","unstructured":"Ismael MR, Abdel-Qader I. Brain tumor classification via statistical features and back-propagation neural network. In: 2018 IEEE International Conference on Electro\/information Technology (EIT). IEEE. 2018;0252\u20130257.","DOI":"10.1109\/EIT.2018.8500308"},{"key":"247_CR30","doi-asserted-by":"publisher","first-page":"118","DOI":"10.1016\/j.patrec.2017.10.036","volume":"139","author":"J Amin","year":"2020","unstructured":"Amin J, Sharif M, Yasmin M, Fernandes SL. A distinctive approach in brain tumor detection and classification using MRI. Pattern Recogn Lett. 2020;139:118\u201327.","journal-title":"Pattern Recogn Lett"},{"issue":"1","key":"247_CR31","doi-asserted-by":"publisher","first-page":"68","DOI":"10.1016\/j.fcij.2017.12.001","volume":"3","author":"H Mohsen","year":"2018","unstructured":"Mohsen H, El-Dahshan E-SA, El-Horbaty E-SM, Salem A-BM. Classification using deep learning neural networks for brain tumors. Future Comput Inform J. 2018;3(1):68\u201371.","journal-title":"Future Comput Inform J"},{"key":"247_CR32","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2019.103345","volume":"111","author":"S Deepak","year":"2019","unstructured":"Deepak S, Ameer P. Brain tumor classification using deep CNN features via transfer learning. Comput Biol Med. 2019;111: 103345.","journal-title":"Comput Biol Med"},{"key":"247_CR33","doi-asserted-by":"publisher","first-page":"5998","DOI":"10.48550\/arXiv.1706.03762","volume":"30","author":"A Vaswani","year":"2017","unstructured":"Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Kaiser L, Polosukhin I. Attention is all you need. Adv Neural Inform Process Syst (NeurIPS). 2017;30:5998\u20136008. https:\/\/doi.org\/10.48550\/arXiv.1706.03762.","journal-title":"Adv Neural Inform Process Syst (NeurIPS)"},{"key":"247_CR34","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.2010.11929","author":"A Dosovitskiy","year":"2020","unstructured":"Dosovitskiy A. An image is worth 16x16 words: transformers for image recognition at scale. ArXiv. 2020. https:\/\/doi.org\/10.48550\/arXiv.2010.11929.","journal-title":"ArXiv"},{"issue":"6088","key":"247_CR35","doi-asserted-by":"publisher","first-page":"533","DOI":"10.1038\/323533a0","volume":"323","author":"DE Rumelhart","year":"1986","unstructured":"Rumelhart DE, Hinton GE, Williams RJ. Learning representations by back propagating errors. Nature. 1986;323(6088):533\u20136. https:\/\/doi.org\/10.1038\/323533a0.","journal-title":"Nature"},{"issue":"8","key":"247_CR36","doi-asserted-by":"publisher","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","volume":"9","author":"S Hochreiter","year":"1997","unstructured":"Hochreiter S, Schmidhuber J. Long short-term memory. Neural Comput. 1997;9(8):1735\u201380. https:\/\/doi.org\/10.1162\/neco.1997.9.8.1735.","journal-title":"Neural Comput"},{"issue":"5786","key":"247_CR37","doi-asserted-by":"publisher","first-page":"504","DOI":"10.1126\/science.1127647","volume":"313","author":"GE Hinton","year":"2006","unstructured":"Hinton GE, Salakhutdinov RR. Reducing the dimensionality of data with neural networks. Science. 2006;313(5786):504\u20137. https:\/\/doi.org\/10.1126\/science.1127647.","journal-title":"Science"},{"issue":"2","key":"247_CR38","doi-asserted-by":"publisher","first-page":"23007","DOI":"10.1002\/ima.23007","volume":"34","author":"AA Joshi","year":"2024","unstructured":"Joshi AA, Aziz RM. Deep learning approach for brain tumor classification using metaheuristic optimization with gene expression data. Int J Imaging Syst Technol. 2024;34(2):23007.","journal-title":"Int J Imaging Syst Technol"},{"issue":"1","key":"247_CR39","doi-asserted-by":"publisher","first-page":"95","DOI":"10.1007\/s00521-022-07204-6","volume":"36","author":"MI Sharif","year":"2024","unstructured":"Sharif MI, Li JP, Khan MA, Kadry S, Tariq U. M3btcnet: multi model brain tumor classification using metaheuristic deep neural network features optimization. Neural Comput Appl. 2024;36(1):95\u2013110.","journal-title":"Neural Comput Appl"},{"key":"247_CR40","doi-asserted-by":"crossref","unstructured":"Patel SK, Singh A. Task scheduling in cloud computing using hybrid metaheuristic: a review. In: Proceedings of the International Conference on Paradigms of Computing, Communication and Data Sciences: PCCDS 2020. Springer. 2021;453\u2013472.","DOI":"10.1007\/978-981-15-7533-4_35"},{"key":"247_CR41","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2023.105419","volume":"87","author":"M Geetha","year":"2024","unstructured":"Geetha M, Srinadh V, Janet J, Sumathi S. Hybrid archimedes sine cosine optimization enabled deep learning for multilevel brain tumor classification using MRI images. Biomed Signal Process Control. 2024;87: 105419.","journal-title":"Biomed Signal Process Control"},{"key":"247_CR42","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2023.105716","volume":"89","author":"A Priya","year":"2024","unstructured":"Priya A, Vasudevan V. Brain tumor classification and detection via hybrid alexnet-gru based on deep learning. Biomed Signal Process Control. 2024;89: 105716.","journal-title":"Biomed Signal Process Control"},{"issue":"7","key":"247_CR43","first-page":"20","volume":"12","author":"BV Subbayamma","year":"2023","unstructured":"Subbayamma BV, Nandhini N, Maurya MST, Ramasamy S, Maheshwari S. Deep learning based brain tumor analysis with manual layer selection. Int J Intell Syst Appl Eng. 2023;12(7):20\u20135.","journal-title":"Int J Intell Syst Appl Eng"},{"key":"247_CR44","doi-asserted-by":"publisher","first-page":"542","DOI":"10.1016\/j.procs.2023.01.036","volume":"218","author":"S Sangui","year":"2023","unstructured":"Sangui S, Iqbal T, Chandra PC, Ghosh SK, Ghosh A. 3d MRI segmentation using u-net architecture for the detection of brain tumor. Procedia Comput Sci. 2023;218:542\u201353.","journal-title":"Procedia Comput Sci"},{"issue":"2","key":"247_CR45","doi-asserted-by":"publisher","first-page":"181","DOI":"10.3390\/jpm13020181","volume":"13","author":"SZ Kurdi","year":"2023","unstructured":"Kurdi SZ, Ali MH, Jaber MM, Saba T, Rehman A, Dama\u02c7sevi\u02c7cius R. Brain tumor classification using meta-heuristic optimized convolutional neural networks. J Personal Med. 2023;13(2):181.","journal-title":"J Personal Med"},{"key":"247_CR46","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2022.109631","volume":"129","author":"P Razzaghi","year":"2022","unstructured":"Razzaghi P, Abbasi K, Shirazi M, Rashidi S. Multimodal brain tumor detection using multimodal deep transfer learning. Appl Soft Comput. 2022;129: 109631.","journal-title":"Appl Soft Comput"},{"issue":"1","key":"247_CR47","doi-asserted-by":"publisher","first-page":"10","DOI":"10.3390\/computers11010010","volume":"11","author":"DR Nayak","year":"2022","unstructured":"Nayak DR, Padhy N, Mallick PK, Bagal DK, Kumar S. Brain tumour classification using noble deep learning approach with parametric optimization through metaheuristics approaches. Computers. 2022;11(1):10.","journal-title":"Computers"},{"issue":"10","key":"247_CR48","doi-asserted-by":"publisher","first-page":"7498","DOI":"10.3390\/curroncol29100590","volume":"29","author":"S Tummala","year":"2022","unstructured":"Tummala S, Kadry S, Bukhari SAC, Rauf HT. Classification of brain tumor from magnetic resonance imaging using vision transformers ensembling. Curr Oncol. 2022;29(10):7498\u2013511.","journal-title":"Curr Oncol"},{"issue":"5","key":"247_CR49","doi-asserted-by":"publisher","first-page":"1240","DOI":"10.1109\/TMI.2016.2538465","volume":"35","author":"S Pereira","year":"2016","unstructured":"Pereira S, Pinto A, Alves V, Silva CA. Brain tumor segmentation using convolutional neural networks in MRI images. IEEE Trans Med Imaging. 2016;35(5):1240\u201351.","journal-title":"IEEE Trans Med Imaging"},{"key":"247_CR50","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2023.107723","volume":"168","author":"R Ranjbarzadeh","year":"2024","unstructured":"Ranjbarzadeh R, Zarbakhsh P, Caputo A, Tirkolaee EB, Bendechache M. Brain tumor segmentation based on optimized convolutional neural network and improved chimp optimization algorithm. Comput Biol Med. 2024;168: 107723.","journal-title":"Comput Biol Med"},{"key":"247_CR51","doi-asserted-by":"publisher","DOI":"10.3174\/ajnr.A8293","author":"A Vossough","year":"2024","unstructured":"Vossough A, Khalili N, Familiar AM, Gandhi D, Viswanathan K, Tu W, Haldar D, Bagheri S, Anderson H, Haldar S, et al. Training and comparison of nnU-net and DeepMedic methods for autosegmentation of pediatric brain tumors. Am J Neuroradiol. 2024. https:\/\/doi.org\/10.3174\/ajnr.A8293.","journal-title":"Am J Neuroradiol"},{"key":"247_CR52","doi-asserted-by":"publisher","first-page":"230254","DOI":"10.1148\/ryai.230254","volume":"6","author":"A Boyd","year":"2024","unstructured":"Boyd A, Ye Z, Prabhu S, Tjong MC, Zha Y, Zapaischykova A, Vajapeyam S, Catalano PJ, Hayat H, Chopra R, et al. Stepwise transfer learning for expert-level pediatric brain tumor MRI segmentation in a limited data scenario. Radiol Artif Intell. 2024;6:230254.","journal-title":"Radiol Artif Intell"},{"issue":"3s","key":"247_CR53","first-page":"179","volume":"12","author":"SN Devi","year":"2023","unstructured":"Devi SN, Gnanamanoharan E, Chinnadurai M. Brain tumour segmentation using adaptive deep convolutional neural network system. Int J Intell Syst Appl Eng. 2023;12(3s):179.","journal-title":"Int J Intell Syst Appl Eng"},{"key":"247_CR54","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-76163-8_24","author":"F Maani","year":"2024","unstructured":"Maani F, Hashmi AUR, Aljuboory M, Saeed N, Sobirov I, Yaqub M. Advanced tumor segmentation in medical imaging: an ensemble approach for brats 2023 adult glioma and pediatric tumor tasks. ArXiv. 2024. https:\/\/doi.org\/10.1007\/978-3-031-76163-8_24.","journal-title":"ArXiv"},{"key":"247_CR55","first-page":"463","volume-title":"International MICCAI brainlesion workshop","author":"MM Rahman","year":"2021","unstructured":"Rahman MM, Sadique MS, Temtam AG, Farzana W, Vidyaratne L, Iftekharuddin KM. Brain tumor segmentation using UNet context encoding network. In: International MICCAI brainlesion workshop. Cham: Springer; 2021. p. 463\u201372."},{"key":"247_CR56","doi-asserted-by":"crossref","unstructured":"Hasan N, Ahmed MF, Nasif MA, Haq MR, Rahman M. 2024. Hybrid feature extraction approach for robust brain tumor classification: Hog, GLCM, and artificial neural network. In: 2024 6th International Conference on Electrical Engineering and Information & Communication Technology (ICEEICT). IEEE. 2024;1292\u20131297.","DOI":"10.1109\/ICEEICT62016.2024.10534587"},{"key":"247_CR57","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2024.106334","volume":"94","author":"SB Shaheema","year":"2024","unstructured":"Shaheema SB, Muppalaneni NB, et al. Explainability based panoptic brain tumor segmentation using a hybrid pa-net with gcnn-resnet50. Biomed Signal Process Control. 2024;94: 106334.","journal-title":"Biomed Signal Process Control"},{"issue":"3","key":"247_CR58","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 TR, Alazab M, Piran MJ. Vision transformers, ensemble model, and transfer learning leveraging explainable AI for brain tumor detection and classification. IEEE J Biomed Health Inform. 2023;28(3):1261\u201372.","journal-title":"IEEE J Biomed Health Inform"},{"key":"247_CR59","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.2007.07588","author":"HJ Weerts","year":"2020","unstructured":"Weerts HJ, Mueller AC, Vanschoren J. Importance of tuning hyperparameters of machine learning algorithms. ArXiv. 2020. https:\/\/doi.org\/10.48550\/arXiv.2007.07588.","journal-title":"ArXiv"},{"key":"247_CR60","doi-asserted-by":"publisher","first-page":"494","DOI":"10.3390\/healthcare10030494","volume":"10","author":"M Ait Amou","year":"2022","unstructured":"Ait Amou M, Xia K, Kamhi S, Mouhafid M. A novel mri diagnosis method for brain tumor classification based on cnn and bayesian optimization. Healthcare. 2022;10:494.","journal-title":"Healthcare"},{"issue":"7553","key":"247_CR61","doi-asserted-by":"publisher","first-page":"436","DOI":"10.1038\/nature14539","volume":"521","author":"Y LeCun","year":"2015","unstructured":"LeCun Y, Bengio Y, Hinton G. Deep learning. Nature. 2015;521(7553):436\u201344.","journal-title":"Nature"},{"key":"247_CR62","first-page":"3320","volume-title":"Neural Information processing systems (NIPS)","author":"J Yosinski","year":"2014","unstructured":"Yosinski J, Clune J, Nguyen A, Fuchs J, Lipson H. How transferable are features in deep neural networks? In: Neural Information processing systems (NIPS). Cambridge: MIT Press; 2014. p. 3320\u20138."},{"key":"247_CR63","doi-asserted-by":"publisher","first-page":"59099","DOI":"10.1109\/ACCESS.2022.3179376","volume":"10","author":"S Ahmad","year":"2022","unstructured":"Ahmad S, Choudhury PK. On the performance of deep transfer learning networks for brain tumor detection using MR images. IEEE Access. 2022;10:59099\u2013114.","journal-title":"IEEE Access"},{"issue":"1","key":"247_CR64","first-page":"3264367","volume":"2022","author":"C Srinivas","year":"2022","unstructured":"Srinivas C, Nandini PK, Zakariah SM, Alothaibi YA, Shaukat K, Partibane B, Awal H. Deep transfer learning approaches in performance analysis of brain tumor classification using MRI images. J Healthcare Eng. 2022;2022(1):3264367.","journal-title":"J Healthcare Eng"},{"key":"247_CR65","doi-asserted-by":"publisher","first-page":"34716","DOI":"10.1109\/ACCESS.2022.3153306","volume":"10","author":"S Asif","year":"2022","unstructured":"Asif S, Yi W, Ain QU, Hou J, Yi T, Si J. Improving effectiveness of different deep transfer learning-based models for detecting brain tumors from MR images. IEEE Access. 2022;10:34716\u201330.","journal-title":"IEEE Access"},{"issue":"2","key":"247_CR66","doi-asserted-by":"publisher","first-page":"757","DOI":"10.1007\/s00034-019-01246-3","volume":"39","author":"A Rehman","year":"2020","unstructured":"Rehman A, Naz S, Razzak MI, Akram F, Imran M. A deep learning- based framework for automatic brain tumors classification using transfer learning. Circuits Syst Signal Process. 2020;39(2):757\u201375.","journal-title":"Circuits Syst Signal Process"},{"key":"247_CR67","doi-asserted-by":"crossref","unstructured":"Chelghoum R, Ikhlef A, Hameurlaine A, Jacquir S. Transfer learning using convolutional neural network architectures for brain tumor classification from MRI images. In: IFIP International Conference on Artificial Intelligence Applications and Innovations, Springer. 2020;189\u2013200.","DOI":"10.1007\/978-3-030-49161-1_17"},{"key":"247_CR68","doi-asserted-by":"publisher","first-page":"17809","DOI":"10.1109\/ACCESS.2019.2892455","volume":"7","author":"ZNK Swati","year":"2019","unstructured":"Swati ZNK, Zhao Q, Kabir M, Ali F, Ali Z, Ahmed S, Lu J. Content- based brain tumor retrieval for MR images using transfer learning. IEEE Access. 2019;7:17809\u201322.","journal-title":"IEEE Access"},{"issue":"1","key":"247_CR69","first-page":"6615468","volume":"2024","author":"RF Jader","year":"2024","unstructured":"Jader RF, Kareem SW, Awla HQ. Ensemble deep learning technique for detecting MRI brain tumor. Appl Comput Intell Soft Comput. 2024;2024(1):6615468.","journal-title":"Appl Comput Intell Soft Comput"},{"issue":"8","key":"247_CR70","doi-asserted-by":"publisher","first-page":"163","DOI":"10.3390\/jimaging9080163","volume":"9","author":"F Khan","year":"2023","unstructured":"Khan F, Ayoub S, Gulzar Y, Majid M, Reegu FA, Mir MS, Soomro AB, Elwasila O. MRI-based effective ensemble frameworks for predicting human brain tumor. J Imaging. 2023;9(8):163.","journal-title":"J Imaging"},{"issue":"3","key":"247_CR71","doi-asserted-by":"publisher","first-page":"481","DOI":"10.3390\/diagnostics13030481","volume":"13","author":"GS Tandel","year":"2023","unstructured":"Tandel GS, Tiwari A, Kakde OG, Gupta N, Saba L, Suri JS. Role of ensemble deep learning for brain tumor classification in multiple magnetic resonance imaging sequence data. Diagnostics. 2023;13(3):481.","journal-title":"Diagnostics"},{"issue":"2","key":"247_CR72","doi-asserted-by":"publisher","first-page":"564","DOI":"10.3390\/app11020564","volume":"11","author":"\u00c1 Gy\u0151rfi","year":"2021","unstructured":"Gy\u0151rfi \u00c1, Szil\u00e1gyi L, Kov\u00e1cs L. A fully automatic procedure for brain tumor segmentation from multi-spectral MRI records using ensemble learning and atlas- based data enhancement. Appl Sci. 2021;11(2):564.","journal-title":"Appl Sci"},{"key":"247_CR73","doi-asserted-by":"publisher","DOI":"10.1016\/j.compmedimag.2019.101660","volume":"79","author":"J Dolz","year":"2020","unstructured":"Dolz J, Desrosiers C, Wang L, Yuan J, Shen D, Ayed IB. Deep cnn ensembles and suggestive annotations for infant brain MRI segmentation. Comput Med Imaging Graph. 2020;79: 101660.","journal-title":"Comput Med Imaging Graph"},{"key":"247_CR74","doi-asserted-by":"publisher","first-page":"117026","DOI":"10.1016\/j.neuroimage.2020.117026","volume":"219","author":"P Coup\u00e9","year":"2020","unstructured":"Coup\u00e9 P, Mansencal B, Cl\u00e9ment M, Giraud R, Denis B, de Senneville V-T, Ta VL, Manjon JV. Assemblynet: a large ensemble of CNNs for 3d whole brain MRI segmentation. Neuroimage. 2020;219:117026.","journal-title":"Neuroimage"},{"key":"247_CR75","doi-asserted-by":"crossref","unstructured":"Gy\u02ddorfi A\u00b4, Kov\u00b4acs L, Szil\u00b4agyi L. Brain tumor detection and segmentation from magnetic resonance image data using ensemble learning methods. In: 2019 IEEE International Conference on Systems, Man and Cybernetics (SMC), IEEE. 2019;909\u2013914","DOI":"10.1109\/SMC.2019.8914463"},{"key":"247_CR76","doi-asserted-by":"publisher","first-page":"115","DOI":"10.1016\/j.bspc.2018.06.003","volume":"47","author":"N Gupta","year":"2019","unstructured":"Gupta N, Bhatele P, Khanna P. Glioma detection on brain MRIs using texture and morphological features with ensemble learning. Biomed Signal Process Control. 2019;47:115\u201325.","journal-title":"Biomed Signal Process Control"},{"key":"247_CR77","doi-asserted-by":"crossref","unstructured":"Karouzos CF. Unsupervised domain adaptation for natural language processing. 2020.","DOI":"10.18653\/v1\/2021.naacl-main.203"},{"issue":"9","key":"247_CR78","doi-asserted-by":"publisher","first-page":"0310748","DOI":"10.1371\/journal.pone.0310748","volume":"19","author":"P Roy","year":"2024","unstructured":"Roy P, Srijon FMS, Bhowmik P. An explainable ensemble approach for advanced brain tumor classification applying dual-gan mechanism and feature extraction techniques over highly imbalanced data. PLoS ONE. 2024;19(9):0310748.","journal-title":"PLoS ONE"},{"key":"247_CR79","doi-asserted-by":"crossref","unstructured":"Al Noman A, Arif ASM. Brain tumor recognition from MRI using deep learning with data balancing methods and its explainability with AI. In: International Conference on Image Processing and Capsule Networks. Springer. 2023;523\u2013538","DOI":"10.1007\/978-981-99-7093-3_35"},{"key":"247_CR80","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.2308.00608","author":"MTR Shawon","year":"2023","unstructured":"Shawon MTR, Shibli G, Ahmed F, Joy SKS. Explainable cost-sensitive deep neural networks for brain tumor detection from brain mri images considering data imbalance. ArXiv. 2023. https:\/\/doi.org\/10.48550\/arXiv.2308.00608.","journal-title":"ArXiv"},{"key":"247_CR81","doi-asserted-by":"publisher","first-page":"919779","DOI":"10.3389\/fninf.2022.919779","volume":"16","author":"P Saat","year":"2022","unstructured":"Saat P, Nogovitsyn N, Hassan MY, Ganaie MA, Souza R, Hemmati H. A domain adaptation benchmark for t1-weighted brain magnetic resonance image segmentation. Front Neuroinform. 2022;16:919779.","journal-title":"Front Neuroinform"}],"container-title":["Discover Artificial Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s44163-025-00247-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s44163-025-00247-3\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s44163-025-00247-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,4,1]],"date-time":"2025-04-01T08:43:43Z","timestamp":1743497023000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s44163-025-00247-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,3,27]]},"references-count":81,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["247"],"URL":"https:\/\/doi.org\/10.1007\/s44163-025-00247-3","relation":{},"ISSN":["2731-0809"],"issn-type":[{"value":"2731-0809","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,3,27]]},"assertion":[{"value":"18 December 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"5 March 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 March 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":"Research involving human and\/or animals"}},{"value":"Informed consent is not applicable as no new human participants were involved in this study.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Informed consent"}},{"value":"The authors declare no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"30"}}