{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,6]],"date-time":"2026-07-06T09:14:16Z","timestamp":1783329256581,"version":"3.54.6"},"reference-count":251,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,12,1]],"date-time":"2026-12-01T00:00:00Z","timestamp":1796083200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,12,1]],"date-time":"2026-12-01T00:00:00Z","timestamp":1796083200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,12,1]],"date-time":"2026-12-01T00:00:00Z","timestamp":1796083200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,12,1]],"date-time":"2026-12-01T00:00:00Z","timestamp":1796083200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,12,1]],"date-time":"2026-12-01T00:00:00Z","timestamp":1796083200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,12,1]],"date-time":"2026-12-01T00:00:00Z","timestamp":1796083200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,12,1]],"date-time":"2026-12-01T00:00:00Z","timestamp":1796083200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Displays"],"published-print":{"date-parts":[[2026,12]]},"DOI":"10.1016\/j.displa.2026.103594","type":"journal-article","created":{"date-parts":[[2026,7,4]],"date-time":"2026-07-04T06:41:14Z","timestamp":1783147274000},"page":"103594","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["Evolution of brain tumor segmentation: A multimodal imaging analysis from machine learning to emerging trends (2014\u20132025)"],"prefix":"10.1016","volume":"95","author":[{"given":"Syed Fakhar","family":"Bilal","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jianqiang","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jun","family":"Qian","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Saqib","family":"Ali","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rooha","family":"Khurram","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Muhammad","family":"Arif","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.displa.2026.103594_b1","doi-asserted-by":"crossref","first-page":"803","DOI":"10.1007\/s00401-016-1545-1","article-title":"The 2016 world health organization classification of tumors of the central nervous system: a summary","volume":"131","author":"Louis","year":"2016","journal-title":"Acta Neuropathol."},{"issue":"2","key":"10.1016\/j.displa.2026.103594_b2","doi-asserted-by":"crossref","first-page":"267","DOI":"10.1007\/s12553-020-00514-6","article-title":"MRI brain tumor medical images analysis using deep learning techniques: a systematic review","volume":"11","author":"Al-Galal","year":"2021","journal-title":"Health Technol."},{"key":"10.1016\/j.displa.2026.103594_b3","doi-asserted-by":"crossref","first-page":"317","DOI":"10.1016\/j.procs.2016.09.407","article-title":"Review of MRI-based brain tumor image segmentation using deep learning methods","volume":"102","author":"I\u015f\u0131n","year":"2016","journal-title":"Procedia Comput. Sci."},{"issue":"1","key":"10.1016\/j.displa.2026.103594_b4","article-title":"Cnn based multiclass brain tumor detection using medical imaging","volume":"2022","author":"Tiwari","year":"2022","journal-title":"Comput. Intell. Neurosci."},{"issue":"5","key":"10.1016\/j.displa.2026.103594_b5","doi-asserted-by":"crossref","first-page":"1240","DOI":"10.1109\/TMI.2016.2538465","article-title":"Brain tumor segmentation using convolutional neural networks in MRI images","volume":"35","author":"Pereira","year":"2016","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"9","key":"10.1016\/j.displa.2026.103594_b6","article-title":"Advance brain tumor segmentation using feature fusion methods with deep U-net model with CNN for MRI data","volume":"35","author":"Nizamani","year":"2023","journal-title":"J. King Saud University-Computer Inf. Sci."},{"key":"10.1016\/j.displa.2026.103594_b7","doi-asserted-by":"crossref","DOI":"10.1016\/j.bspc.2023.104988","article-title":"Brain tumor classification utilizing deep features derived from high-quality regions in MRI images","volume":"85","author":"Aamir","year":"2023","journal-title":"Biomed. Signal Process. Control."},{"key":"10.1016\/j.displa.2026.103594_b8","doi-asserted-by":"crossref","DOI":"10.1016\/j.neucom.2025.130162","article-title":"Learning deep feature representations for multi-modal MR brain tumor segmentation","volume":"638","author":"Zhou","year":"2025","journal-title":"Neurocomputing"},{"key":"10.1016\/j.displa.2026.103594_b9","doi-asserted-by":"crossref","DOI":"10.1016\/j.bspc.2026.109603","article-title":"Deep feature-based approaches for brain tumor classification and segmentation in medical imaging","volume":"117","author":"Yadav","year":"2026","journal-title":"Biomed. Signal Process. Control."},{"key":"10.1016\/j.displa.2026.103594_b10","doi-asserted-by":"crossref","first-page":"118","DOI":"10.1016\/j.patrec.2017.10.036","article-title":"A distinctive approach in brain tumor detection and classification using MRI","volume":"139","author":"Amin","year":"2020","journal-title":"Pattern Recognit. Lett."},{"issue":"31","key":"10.1016\/j.displa.2026.103594_b11","doi-asserted-by":"crossref","DOI":"10.1097\/MD.0000000000011256","article-title":"Comparisons of the accuracy of radiation diagnostic modalities in brain tumor: A nonrandomized, nonexperimental, cross-sectional trial","volume":"97","author":"Luo","year":"2018","journal-title":"Medicine"},{"issue":"1","key":"10.1016\/j.displa.2026.103594_b12","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1007\/s00530-025-02075-y","article-title":"Explainable deep learning framework for brain tumor segmentation using vision transformer and conditional random fields","volume":"32","author":"Safarpour","year":"2026","journal-title":"Multimedia Syst."},{"issue":"8","key":"10.1016\/j.displa.2026.103594_b13","doi-asserted-by":"crossref","first-page":"2157","DOI":"10.1007\/s00371-020-01977-4","article-title":"Computer-aided diagnostic network for brain tumor classification employing modulated gabor filter banks","volume":"37","author":"Singh","year":"2021","journal-title":"Vis. Comput."},{"key":"10.1016\/j.displa.2026.103594_b14","doi-asserted-by":"crossref","first-page":"899","DOI":"10.1007\/s11042-020-09786-6","article-title":"Towards a computer aided diagnosis (CAD) for brain MRI glioblastomas tumor exploration based on a deep convolutional neuronal network (D-CNN) architecture","volume":"80","author":"Mzoughi","year":"2021","journal-title":"Multimedia Tools Appl."},{"issue":"1","key":"10.1016\/j.displa.2026.103594_b15","article-title":"Computer-aided brain tumor diagnosis performance evaluation of deep learner CNN using augmented brain MRI","volume":"2021","author":"Naseer","year":"2021","journal-title":"Int. J. Biomed. Imaging"},{"key":"10.1016\/j.displa.2026.103594_b16","series-title":"2022 Muthanna International Conference on Engineering Science and Technology","first-page":"43","article-title":"Brain tumor segmentation utilizing thresholding and K-means clustering","author":"Khilkhal","year":"2022"},{"key":"10.1016\/j.displa.2026.103594_b17","series-title":"2023 Global Conference on Information Technologies and Communications","first-page":"1","article-title":"Enhancing network performance in LEO satellite networks through DRL-based routing optimization","author":"BK","year":"2023"},{"key":"10.1016\/j.displa.2026.103594_b18","series-title":"Proceedings of International Conference on Information and Communication Technology for Development: ICICTD 2022","first-page":"223","article-title":"Classification and segmentation on multi-regional brain tumors using volumetric images of MRI with customized 3D U-net framework","author":"Faysal Ahamed","year":"2023"},{"key":"10.1016\/j.displa.2026.103594_b19","article-title":"Attention is all you need","volume":"vol. 30","author":"Vaswani","year":"2017"},{"issue":"2","key":"10.1016\/j.displa.2026.103594_b20","doi-asserted-by":"crossref","first-page":"852","DOI":"10.21037\/qims-20-595","article-title":"Federated learning: a collaborative effort to achieve better medical imaging models for individual sites that have small labelled datasets","volume":"11","author":"Ng","year":"2021","journal-title":"Quant. Imaging Med. Surg."},{"key":"10.1016\/j.displa.2026.103594_b21","doi-asserted-by":"crossref","DOI":"10.1016\/j.media.2021.102298","article-title":"Federated learning for computational pathology on gigapixel whole slide images","volume":"76","author":"Lu","year":"2022","journal-title":"Med. Image Anal."},{"key":"10.1016\/j.displa.2026.103594_b22","doi-asserted-by":"crossref","DOI":"10.1016\/j.compeleceng.2022.107818","article-title":"A review on federated learning towards image processing","volume":"99","author":"KhoKhar","year":"2022","journal-title":"Comput. Electr. Eng."},{"key":"10.1016\/j.displa.2026.103594_b23","doi-asserted-by":"crossref","DOI":"10.1016\/j.compeleceng.2025.110099","article-title":"Advancements in medical image segmentation: A review of transformer models","volume":"123","author":"Kumar","year":"2025","journal-title":"Comput. Electr. Eng."},{"key":"10.1016\/j.displa.2026.103594_b24","doi-asserted-by":"crossref","DOI":"10.1016\/j.jneumeth.2025.110424","article-title":"Brain tumor segmentation with deep learning: Current approaches and future perspectives","author":"Verma","year":"2025","journal-title":"J. Neurosci. Methods"},{"issue":"1","key":"10.1016\/j.displa.2026.103594_b25","doi-asserted-by":"crossref","first-page":"64","DOI":"10.1007\/s40747-024-01639-1","article-title":"A survey of MRI-based brain tissue segmentation using deep learning","volume":"11","author":"Wu","year":"2025","journal-title":"Complex Intell. Syst."},{"key":"10.1016\/j.displa.2026.103594_b26","doi-asserted-by":"crossref","DOI":"10.2196\/57723","article-title":"Transformers for neuroimage segmentation: scoping review","volume":"27","author":"Iratni","year":"2025","journal-title":"J. Med. Internet Res."},{"issue":"1","key":"10.1016\/j.displa.2026.103594_b27","doi-asserted-by":"crossref","first-page":"2","DOI":"10.1038\/s41698-024-00789-2","article-title":"A review of deep learning for brain tumor analysis in MRI","volume":"9","author":"Dorfner","year":"2025","journal-title":"NPJ Precis. Oncol."},{"issue":"3","key":"10.1016\/j.displa.2026.103594_b28","doi-asserted-by":"crossref","first-page":"1525","DOI":"10.1007\/s11831-024-10188-2","article-title":"A critical review on segmentation of glioma brain tumor and prediction of overall survival","volume":"32","author":"Rasool","year":"2025","journal-title":"Arch. Comput. Methods Eng."},{"issue":"9","key":"10.1016\/j.displa.2026.103594_b29","doi-asserted-by":"crossref","first-page":"2746","DOI":"10.3390\/s25092746","article-title":"Advanced deep learning and machine learning techniques for MRI brain tumor analysis: A review","volume":"25","author":"Missaoui","year":"2025","journal-title":"Sensors"},{"issue":"1","key":"10.1016\/j.displa.2026.103594_b30","first-page":"66","article-title":"CNN-based image segmentation approach in brain tumor classification: A review","volume":"84","author":"Huda","year":"2025","journal-title":"Eng. Proc."},{"issue":"6","key":"10.1016\/j.displa.2026.103594_b31","doi-asserted-by":"crossref","first-page":"1838","DOI":"10.3390\/s25061838","article-title":"Advancing precision: A comprehensive review of MRI segmentation datasets from brats challenges (2012\u20132025)","volume":"25","author":"Bonato","year":"2025","journal-title":"Sensors (Basel, Switzerland)"},{"issue":"10","key":"10.1016\/j.displa.2026.103594_b32","doi-asserted-by":"crossref","first-page":"1993","DOI":"10.1109\/TMI.2014.2377694","article-title":"The multimodal brain tumor image segmentation benchmark (BRATS)","volume":"34","author":"Menze","year":"2014","journal-title":"IEEE Trans. Med. Imaging"},{"key":"10.1016\/j.displa.2026.103594_b33","first-page":"2278","article-title":"MRI brain tumor segmentation using genetic algorithm with svm classifier","author":"Aswathy","year":"2017","journal-title":"J. Electron. Commun. Eng. E-ISSN"},{"key":"10.1016\/j.displa.2026.103594_b34","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1016\/j.image.2017.05.013","article-title":"A non-invasive and adaptive CAD system to detect brain tumor from T2-weighted MRIs using customized otsu\u2019s thresholding with prominent features and supervised learning","volume":"59","author":"Gupta","year":"2017","journal-title":"Signal Process., Image Commun."},{"key":"10.1016\/j.displa.2026.103594_b35","doi-asserted-by":"crossref","first-page":"183","DOI":"10.1007\/s11548-016-1483-3","article-title":"Automated brain tumour detection and segmentation using superpixel-based extremely randomized trees in FLAIR MRI","volume":"12","author":"Soltaninejad","year":"2017","journal-title":"Int. J. Comput. Assist. Radiol. Surg."},{"key":"10.1016\/j.displa.2026.103594_b36","doi-asserted-by":"crossref","first-page":"111","DOI":"10.1016\/j.jneumeth.2016.06.017","article-title":"Automatic brain tissue segmentation in MR images using random forests and conditional random fields","volume":"270","author":"Pereira","year":"2016","journal-title":"J. Neurosci. Methods"},{"key":"10.1016\/j.displa.2026.103594_b37","doi-asserted-by":"crossref","first-page":"209","DOI":"10.1007\/s12021-014-9245-2","article-title":"Optimal symmetric multimodal templates and concatenated random forests for supervised brain tumor segmentation (simplified) with ANTsR","volume":"13","author":"Tustison","year":"2015","journal-title":"Neuroinformatics"},{"key":"10.1016\/j.displa.2026.103594_b38","series-title":"Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries","first-page":"88","article-title":"Brain tumor segmentation with optimized random forest","author":"Lefkovits","year":"2016"},{"issue":"13","key":"10.1016\/j.displa.2026.103594_b39","doi-asserted-by":"crossref","first-page":"4855","DOI":"10.1088\/0031-9155\/61\/13\/4855","article-title":"ATLAAS: an automatic decision tree-based learning algorithm for advanced image segmentation in positron emission tomography","volume":"61","author":"Berthon","year":"2016","journal-title":"Phys. Med. Biol."},{"key":"10.1016\/j.displa.2026.103594_b40","series-title":"2014 22nd International Conference on Pattern Recognition","first-page":"556","article-title":"Efficient interactive brain tumor segmentation as within-brain kNN classification","author":"Havaei","year":"2014"},{"issue":"1","key":"10.1016\/j.displa.2026.103594_b41","article-title":"Automated feature extraction in brain tumor by magnetic resonance imaging using gaussian mixture models","volume":"2015","author":"Chaddad","year":"2015","journal-title":"Int. J. Biomed. Imaging"},{"key":"10.1016\/j.displa.2026.103594_b42","first-page":"260","article-title":"Statistical feature selection for enhanced detection of brain tumor","volume":"vol. 9217","author":"Chaddad","year":"2014"},{"issue":"3","key":"10.1016\/j.displa.2026.103594_b43","doi-asserted-by":"crossref","DOI":"10.21917\/ijsc.2015.0133","article-title":"Supervised machine learning approaches: a survey.","volume":"5","author":"Muhammad","year":"2015","journal-title":"ICTACT J. Soft Comput."},{"key":"10.1016\/j.displa.2026.103594_b44","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2022.118016","article-title":"An unsupervised domain adaptation brain CT segmentation method across image modalities and diseases","volume":"207","author":"Dong","year":"2022","journal-title":"Expert Syst. Appl."},{"key":"10.1016\/j.displa.2026.103594_b45","doi-asserted-by":"crossref","first-page":"190","DOI":"10.1016\/j.asoc.2015.09.016","article-title":"An unsupervised learning method with a clustering approach for tumor identification and tissue segmentation in magnetic resonance brain images","volume":"38","author":"Vishnuvarthanan","year":"2016","journal-title":"Appl. Soft Comput."},{"key":"10.1016\/j.displa.2026.103594_b46","doi-asserted-by":"crossref","first-page":"753","DOI":"10.1016\/j.nicl.2016.09.021","article-title":"Comparison of unsupervised classification methods for brain tumor segmentation using multi-parametric MRI","volume":"12","author":"Sauwen","year":"2016","journal-title":"NeuroImage: Clin."},{"key":"10.1016\/j.displa.2026.103594_b47","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/1687-5281-2014-21","article-title":"Tumor segmentation in brain MRI using a fuzzy approach with class center priors","volume":"2014","author":"El-Melegy","year":"2014","journal-title":"EURASIP J. Image Video Process."},{"issue":"3","key":"10.1016\/j.displa.2026.103594_b48","article-title":"Brain tumor segmentation using k-means clustering and fuzzy c-means algorithms and its area calculation","volume":"2","author":"Jose","year":"2014","journal-title":"Int. J. Innov. Res. Comput. Commun. Eng."},{"issue":"8","key":"10.1016\/j.displa.2026.103594_b49","doi-asserted-by":"crossref","first-page":"254","DOI":"10.1007\/s10916-019-1366-6","article-title":"Brain tumor detection using depth-first search tree segmentation","volume":"43","author":"Janardhanaprabhu","year":"2019","journal-title":"J. Med. Syst."},{"key":"10.1016\/j.displa.2026.103594_b50","doi-asserted-by":"crossref","first-page":"527","DOI":"10.1016\/j.asoc.2016.01.040","article-title":"Rough possibilistic type-2 fuzzy C-means clustering for MR brain image segmentation","volume":"46","author":"Sarkar","year":"2016","journal-title":"Appl. Soft Comput."},{"key":"10.1016\/j.displa.2026.103594_b51","doi-asserted-by":"crossref","first-page":"808","DOI":"10.1016\/j.amc.2015.01.053","article-title":"Improving the runtime of MRF based method for MRI brain segmentation","volume":"256","author":"Ahmadvand","year":"2015","journal-title":"Appl. Math. Comput."},{"key":"10.1016\/j.displa.2026.103594_b52","series-title":"Modified FCM using genetic algorithm for segmentation of MRI brain images","first-page":"1","author":"Jansi","year":"2014"},{"key":"10.1016\/j.displa.2026.103594_b53","series-title":"2023 3rd International Conference on Digital Futures and Transformative Technologies (ICoDT2)","first-page":"1","article-title":"A novel deep learning framework for the detection of brain tumor","author":"Abbas","year":"2023"},{"key":"10.1016\/j.displa.2026.103594_b54","series-title":"Tumor detection in brain MRI image using template based K-means and fuzzy C-means clustering algorithm","first-page":"1","author":"Ahmmed","year":"2016"},{"key":"10.1016\/j.displa.2026.103594_b55","first-page":"102","article-title":"Brain tissue segmentation in MRI images using GMM","volume":"10","author":"Subashini","year":"2015","journal-title":"Int. J. Appl. Eng. Res"},{"key":"10.1016\/j.displa.2026.103594_b56","series-title":"2016 24th Iranian Conference on Electrical Engineering","first-page":"832","article-title":"Automatic segmentation of multiple sclerosis lesions in brain MRI using constrained GMM and genetic algorithm","author":"Zangeneh","year":"2016"},{"key":"10.1016\/j.displa.2026.103594_b57","doi-asserted-by":"crossref","first-page":"87","DOI":"10.1016\/j.patrec.2017.05.028","article-title":"Entropy based segmentation of tumor from brain MR images\u2013a study with teaching learning based optimization","volume":"94","author":"Rajinikanth","year":"2017","journal-title":"Pattern Recognit. Lett."},{"issue":"4","key":"10.1016\/j.displa.2026.103594_b58","doi-asserted-by":"crossref","first-page":"277","DOI":"10.1002\/ima.22104","article-title":"Improved fuzzy entropy clustering algorithm for MRI brain image segmentation","volume":"24","author":"Verma","year":"2014","journal-title":"Int. J. Imaging Syst. Technol."},{"key":"10.1016\/j.displa.2026.103594_b59","doi-asserted-by":"crossref","first-page":"758","DOI":"10.1016\/j.asoc.2015.05.038","article-title":"Conditional spatial fuzzy C-means clustering algorithm for segmentation of MRI images","volume":"34","author":"Adhikari","year":"2015","journal-title":"Appl. Soft Comput."},{"key":"10.1016\/j.displa.2026.103594_b60","series-title":"2016 International Conference on Advances in Computing, Communications and Informatics","first-page":"657","article-title":"Statistical textural feature and deformable model based MR brain tumor segmentation","author":"Banday","year":"2016"},{"issue":"44","key":"10.1016\/j.displa.2026.103594_b61","doi-asserted-by":"crossref","first-page":"1","DOI":"10.17485\/ijst\/2017\/v10i44\/120574","article-title":"Brain tumor segmentation using watershed technique and self organizing maps","volume":"10","author":"Anand","year":"2017","journal-title":"Indian J. Sci. Technol."},{"key":"10.1016\/j.displa.2026.103594_b62","doi-asserted-by":"crossref","first-page":"117","DOI":"10.1016\/j.ins.2013.10.002","article-title":"Improving MR brain image segmentation using self-organising maps and entropy-gradient clustering","volume":"262","author":"Ortiz","year":"2014","journal-title":"Inform. Sci."},{"issue":"1","key":"10.1016\/j.displa.2026.103594_b63","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1007\/s00138-020-01060-x","article-title":"Deep learning in medical image registration: a survey","volume":"31","author":"Haskins","year":"2020","journal-title":"Mach. Vis. Appl."},{"issue":"1","key":"10.1016\/j.displa.2026.103594_b64","doi-asserted-by":"crossref","first-page":"184","DOI":"10.3390\/biomedicines11010184","article-title":"Computer-aided early melanoma brain-tumor detection using a deep-learning approach","volume":"11","author":"Asad","year":"2023","journal-title":"Biomedicines"},{"key":"10.1016\/j.displa.2026.103594_b65","doi-asserted-by":"crossref","DOI":"10.1016\/j.bspc.2021.102458","article-title":"Brain tumor prediction on MR images with semantic segmentation by using deep learning network and 3D imaging of tumor region","volume":"66","author":"Karayegen","year":"2021","journal-title":"Biomed. Signal Process. Control."},{"key":"10.1016\/j.displa.2026.103594_b66","doi-asserted-by":"crossref","DOI":"10.1038\/s41598-025-33970-z","article-title":"XcepFusion for brain tumor detection using a hybrid transfer learning framework with layer pruning and freezing","author":"Rastogi","year":"2025","journal-title":"Sci. Rep."},{"issue":"1","key":"10.1016\/j.displa.2026.103594_b67","doi-asserted-by":"crossref","first-page":"1437","DOI":"10.1038\/s41598-024-84386-0","article-title":"Deep learning-integrated MRI brain tumor analysis: feature extraction, segmentation, and survival prediction using replicator and volumetric networks","volume":"15","author":"Rastogi","year":"2025","journal-title":"Sci. Rep."},{"issue":"1","key":"10.1016\/j.displa.2026.103594_b68","doi-asserted-by":"crossref","DOI":"10.32604\/iasc.2024.039009","article-title":"Extended deep learning algorithm for improved brain tumor diagnosis system.","volume":"39","author":"Adimoolam","year":"2024","journal-title":"Intell. Autom. Soft Comput."},{"issue":"2","key":"10.1016\/j.displa.2026.103594_b69","doi-asserted-by":"crossref","first-page":"514","DOI":"10.1109\/JBHI.2020.2997760","article-title":"Multi-scale context-guided deep network for automated lesion segmentation with endoscopy images of gastrointestinal tract","volume":"25","author":"Wang","year":"2020","journal-title":"IEEE J. Biomed. Health Inform."},{"issue":"8","key":"10.1016\/j.displa.2026.103594_b70","doi-asserted-by":"crossref","first-page":"8357","DOI":"10.1007\/s12652-020-02568-w","article-title":"Automated categorization of brain tumor from mri using cnn features and svm","volume":"12","author":"Deepak","year":"2021","journal-title":"J. Ambient. Intell. Humaniz. Comput."},{"key":"10.1016\/j.displa.2026.103594_b71","doi-asserted-by":"crossref","first-page":"248","DOI":"10.1016\/j.neucom.2017.12.032","article-title":"Segmentation of glioma tumors in brain using deep convolutional neural network","volume":"282","author":"Hussain","year":"2018","journal-title":"Neurocomputing"},{"issue":"9","key":"10.1016\/j.displa.2026.103594_b72","doi-asserted-by":"crossref","first-page":"3030","DOI":"10.1109\/JSTARS.2018.2846178","article-title":"Deep convolutional neural network for complex wetland classification using optical remote sensing imagery","volume":"11","author":"Rezaee","year":"2018","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote. Sens."},{"issue":"6","key":"10.1016\/j.displa.2026.103594_b73","doi-asserted-by":"crossref","first-page":"404","DOI":"10.1016\/j.jksues.2020.06.001","article-title":"An efficient brain tumor image segmentation based on deep residual networks (ResNets)","volume":"33","author":"Shehab","year":"2021","journal-title":"J. King Saud Univ., Eng. Sci."},{"issue":"9","key":"10.1016\/j.displa.2026.103594_b74","doi-asserted-by":"crossref","first-page":"294","DOI":"10.1007\/s10916-019-1416-0","article-title":"Brain tumor segmentation using convolutional neural networks in MRI images","volume":"43","author":"Thaha","year":"2019","journal-title":"J. Med. Syst."},{"key":"10.1016\/j.displa.2026.103594_b75","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s10916-019-1289-2","article-title":"Brain tumor segmentation based on improved convolutional neural network in combination with non-quantifiable local texture feature","volume":"43","author":"Deng","year":"2019","journal-title":"J. Med. Syst."},{"key":"10.1016\/j.displa.2026.103594_b76","doi-asserted-by":"crossref","first-page":"248","DOI":"10.1016\/j.neucom.2017.12.032","article-title":"Segmentation of glioma tumors in brain using deep convolutional neural network","volume":"282","author":"Hussain","year":"2018","journal-title":"Neurocomputing"},{"key":"10.1016\/j.displa.2026.103594_b77","doi-asserted-by":"crossref","first-page":"39","DOI":"10.1016\/j.cmpb.2018.09.007","article-title":"Fully automatic brain tumor segmentation using end-to-end incremental deep neural networks in MRI images","volume":"166","author":"Saouli","year":"2018","journal-title":"Comput. Methods Programs Biomed."},{"key":"10.1016\/j.displa.2026.103594_b78","doi-asserted-by":"crossref","first-page":"98","DOI":"10.1016\/j.media.2017.10.002","article-title":"A deep learning model integrating FCNNs and CRFs for brain tumor segmentation","volume":"43","author":"Zhao","year":"2018","journal-title":"Med. Image Anal."},{"key":"10.1016\/j.displa.2026.103594_b79","doi-asserted-by":"crossref","first-page":"903","DOI":"10.1007\/s10278-020-00347-9","article-title":"Deep multi-scale 3D convolutional neural network (CNN) for MRI gliomas brain tumor classification","volume":"33","author":"Mzoughi","year":"2020","journal-title":"J. Digit. Imaging"},{"key":"10.1016\/j.displa.2026.103594_b80","series-title":"International MICCAI Brainlesion Workshop","first-page":"57","article-title":"Skull-stripping of glioblastoma MRI scans using 3D deep learning","author":"Thakur","year":"2019"},{"key":"10.1016\/j.displa.2026.103594_b81","doi-asserted-by":"crossref","DOI":"10.1016\/j.bspc.2021.103475","article-title":"A computation-efficient CNN system for high-quality brain tumor segmentation","volume":"74","author":"Sun","year":"2022","journal-title":"Biomed. Signal Process. Control."},{"issue":"16","key":"10.1016\/j.displa.2026.103594_b82","doi-asserted-by":"crossref","first-page":"2650","DOI":"10.3390\/diagnostics13162650","article-title":"Brain tumor segmentation from MRI images using handcrafted convolutional neural network","volume":"13","author":"Ullah","year":"2023","journal-title":"Diagnostics"},{"issue":"5","key":"10.1016\/j.displa.2026.103594_b83","doi-asserted-by":"crossref","first-page":"7599","DOI":"10.1007\/s11042-022-13713-2","article-title":"Human brain tumor classification and segmentation using CNN","volume":"82","author":"Kumar","year":"2023","journal-title":"Multimedia Tools Appl."},{"key":"10.1016\/j.displa.2026.103594_b84","doi-asserted-by":"crossref","DOI":"10.1016\/j.bspc.2024.107220","article-title":"Modified recurrent residual attention U-net model for MRI-based brain tumor segmentation","volume":"102","author":"Yadav","year":"2025","journal-title":"Biomed. Signal Process. Control."},{"key":"10.1016\/j.displa.2026.103594_b85","doi-asserted-by":"crossref","first-page":"25","DOI":"10.3389\/fncom.2020.00025","article-title":"Brain tumor segmentation using an ensemble of 3D U-nets and overall survival prediction using radiomic features","volume":"14","author":"Feng","year":"2020","journal-title":"Front. Comput. Neurosci."},{"issue":"9","key":"10.1016\/j.displa.2026.103594_b86","doi-asserted-by":"crossref","first-page":"3297","DOI":"10.3390\/app10093297","article-title":"Segmentation of intracranial hemorrhage using semi-supervised multi-task attention-based U-net","volume":"10","author":"Wang","year":"2020","journal-title":"Appl. Sci."},{"issue":"12","key":"10.1016\/j.displa.2026.103594_b87","doi-asserted-by":"crossref","first-page":"2203","DOI":"10.3390\/electronics9122203","article-title":"Bu-net: Brain tumor segmentation using modified u-net architecture","volume":"9","author":"Rehman","year":"2020","journal-title":"Electronics"},{"key":"10.1016\/j.displa.2026.103594_b88","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s12859-021-04347-6","article-title":"MRI-based brain tumor segmentation using FPGA-accelerated neural network","volume":"22","author":"Xiong","year":"2021","journal-title":"BMC Bioinformatics"},{"issue":"16","key":"10.1016\/j.displa.2026.103594_b89","doi-asserted-by":"crossref","first-page":"1962","DOI":"10.3390\/electronics10161962","article-title":"RMU-net: a novel residual mobile U-net model for brain tumor segmentation from MR images","volume":"10","author":"Saeed","year":"2021","journal-title":"Electronics"},{"key":"10.1016\/j.displa.2026.103594_b90","doi-asserted-by":"crossref","DOI":"10.1016\/j.cmpb.2021.106208","article-title":"DFP-ResU-net: Convolutional neural network with a dilated convolutional feature pyramid for multimodal brain tumor segmentation","volume":"208","author":"Wang","year":"2021","journal-title":"Comput. Methods Programs Biomed."},{"key":"10.1016\/j.displa.2026.103594_b91","doi-asserted-by":"crossref","first-page":"58533","DOI":"10.1109\/ACCESS.2020.2983075","article-title":"Attention gate resu-net for automatic MRI brain tumor segmentation","volume":"8","author":"Zhang","year":"2020","journal-title":"IEEE Access"},{"issue":"2","key":"10.1016\/j.displa.2026.103594_b92","doi-asserted-by":"crossref","first-page":"661","DOI":"10.1109\/TMI.2020.3034995","article-title":"Inter-slice context residual learning for 3D medical image segmentation","volume":"40","author":"Zhang","year":"2020","journal-title":"IEEE Trans. Med. Imaging"},{"key":"10.1016\/j.displa.2026.103594_b93","article-title":"Using U-net network for efficient brain tumor segmentation in MRI images","volume":"2","author":"Walsh","year":"2022","journal-title":"Heal. Anal."},{"issue":"5","key":"10.1016\/j.displa.2026.103594_b94","doi-asserted-by":"crossref","first-page":"496","DOI":"10.1007\/s42979-024-02799-0","article-title":"Improved brain tumor segmentation using unet-lstm architecture","volume":"5","author":"Sowrirajan","year":"2024","journal-title":"SN Comput. Sci."},{"key":"10.1016\/j.displa.2026.103594_b95","article-title":"Enhancing brain tumor segmentation in MRI images: A hybrid approach using unet, attention mechanisms, and transformers","volume":"27","author":"Nguyen-Tat","year":"2024","journal-title":"Egypt. Inform. J."},{"key":"10.1016\/j.displa.2026.103594_b96","doi-asserted-by":"crossref","DOI":"10.1016\/j.bspc.2022.103647","article-title":"A hybrid DenseNet121-UNet model for brain tumor segmentation from MR images","volume":"76","author":"Cinar","year":"2022","journal-title":"Biomed. Signal Process. Control."},{"issue":"3","key":"10.1016\/j.displa.2026.103594_b97","article-title":"Brain tumor auto-segmentation on multimodal imaging modalities using deep neural network.","volume":"72","author":"Hossain","year":"2022","journal-title":"Comput. Mater. Contin."},{"issue":"1","key":"10.1016\/j.displa.2026.103594_b98","doi-asserted-by":"crossref","first-page":"259","DOI":"10.1111\/coin.12259","article-title":"Brain tumor diagnosis based on artificial neural network and a chaos whale optimization algorithm","volume":"36","author":"Gong","year":"2020","journal-title":"Comput. Intell."},{"key":"10.1016\/j.displa.2026.103594_b99","doi-asserted-by":"crossref","first-page":"15965","DOI":"10.1007\/s00521-019-04650-7","article-title":"Brain tumor detection: a long short-term memory (LSTM)-based learning model","volume":"32","author":"Amin","year":"2020","journal-title":"Neural Comput. Appl."},{"issue":"2","key":"10.1016\/j.displa.2026.103594_b100","doi-asserted-by":"crossref","first-page":"320","DOI":"10.3390\/sym13020320","article-title":"Recurrent multi-fiber network for 3D MRI brain tumor segmentation","volume":"13","author":"Zhao","year":"2021","journal-title":"Symmetry"},{"key":"10.1016\/j.displa.2026.103594_b101","series-title":"2019 IEEE International Conference on Image Processing","first-page":"240","article-title":"LSTM-MA: A LSTM method with multi-modality and adjacency constraint for brain image segmentation","author":"Xie","year":"2019"},{"key":"10.1016\/j.displa.2026.103594_b102","series-title":"2021 Second International Conference on Electronics and Sustainable Communication Systems","first-page":"2018","article-title":"Novel framework for predictive analytics of brain tumor segmentation using recurrent neural network","author":"Paulchamy","year":"2021"},{"issue":"3","key":"10.1016\/j.displa.2026.103594_b103","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3450519","article-title":"Multimodal brain tumor segmentation based on an intelligent U-NET-LSTM algorithm in smart hospitals","volume":"21","author":"Hu","year":"2021","journal-title":"ACM Trans. Internet Technol."},{"key":"10.1016\/j.displa.2026.103594_b104","series-title":"2019 IEEE 4th International Conference on Image, Vision and Computing","first-page":"236","article-title":"Lstm multi-modal unet for brain tumor segmentation","author":"Xu","year":"2019"},{"issue":"1","key":"10.1016\/j.displa.2026.103594_b105","doi-asserted-by":"crossref","first-page":"37","DOI":"10.31803\/tg-20210204162414","article-title":"Bidirectional ConvLSTMXNet for brain tumor segmentation of MR images","volume":"15","author":"Ravikumar","year":"2021","journal-title":"Tehni\u010dki Glas."},{"key":"10.1016\/j.displa.2026.103594_b106","article-title":"Diagnosing the MRI brain tumour images through RNN-LSTM","volume":"9","author":"Amarneni","year":"2024","journal-title":"E-Prime-Advances Electr. Eng. Electron. Energy"},{"key":"10.1016\/j.displa.2026.103594_b107","first-page":"833","article-title":"Brain tumor segmentation using the hybrid CNN & LSTM model","volume":"vol. 7","author":"Jadhav","year":"2024"},{"issue":"3","key":"10.1016\/j.displa.2026.103594_b108","article-title":"Brain tumor detection using RNN","volume":"4","author":"Muvedha","year":"2023","journal-title":"Brain"},{"key":"10.1016\/j.displa.2026.103594_b109","article-title":"Advanced fusion of 3D U-net-LSTM models for accurate brain tumor segmentation","author":"Sajjanar","year":"2024","journal-title":"Int J Adv Comput. Sci Appl"},{"issue":"1","key":"10.1016\/j.displa.2026.103594_b110","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1186\/s13677-024-00675-z","article-title":"Computational intelligence-based classification system for the diagnosis of memory impairment in psychoactive substance users","volume":"13","author":"Zhu","year":"2024","journal-title":"J. Cloud Comput."},{"issue":"4","key":"10.1016\/j.displa.2026.103594_b111","doi-asserted-by":"crossref","first-page":"473","DOI":"10.1108\/SR-01-2018-0008","article-title":"Hybrid active contour model and deep belief network-based approach for brain tumor segmentation and classification","volume":"39","author":"Ratna Raju","year":"2019","journal-title":"Sensor Rev."},{"issue":"10","key":"10.1016\/j.displa.2026.103594_b112","doi-asserted-by":"crossref","first-page":"1912","DOI":"10.3390\/sym15101912","article-title":"A symmetrical approach to brain tumor segmentation in MRI using deep learning and threefold attention mechanism","volume":"15","author":"Rahman","year":"2023","journal-title":"Symmetry"},{"key":"10.1016\/j.displa.2026.103594_b113","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s10916-019-1483-2","article-title":"Brain tumor detection by using stacked autoencoders in deep learning","volume":"44","author":"Amin","year":"2020","journal-title":"J. Med. Syst."},{"key":"10.1016\/j.displa.2026.103594_b114","series-title":"International MICCAI BrainLesion Workshop","first-page":"311","article-title":"3D MRI brain tumor segmentation using autoencoder regularization","author":"Myronenko","year":"2018"},{"key":"10.1016\/j.displa.2026.103594_b115","series-title":"Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries: 5th International Workshop, BrainLes 2019, Held in Conjunction with MICCAI 2019, Shenzhen, China, October 17, 2019, Revised Selected Papers, Part II 5","first-page":"316","article-title":"Multimodal brain tumor segmentation with normal appearance autoencoder","author":"Astaraki","year":"2020"},{"issue":"9","key":"10.1016\/j.displa.2026.103594_b116","doi-asserted-by":"crossref","first-page":"4317","DOI":"10.3390\/app11094317","article-title":"Segmentation of brain tumors from MRI images using convolutional autoencoder","volume":"11","author":"Bad\u017ea","year":"2021","journal-title":"Appl. Sci."},{"issue":"1","key":"10.1016\/j.displa.2026.103594_b117","doi-asserted-by":"crossref","first-page":"71","DOI":"10.1007\/s44163-024-00180-x","article-title":"Segmentation of MR images for brain tumor detection using autoencoder neural network","volume":"4","author":"Hoseini","year":"2024","journal-title":"Discov. Artif. Intell."},{"key":"10.1016\/j.displa.2026.103594_b118","doi-asserted-by":"crossref","DOI":"10.1016\/j.gep.2022.119248","article-title":"Segnet and salp water optimization-driven deep belief network for segmentation and classification of brain tumor","volume":"45","author":"Bidkar","year":"2022","journal-title":"Gene Expr. Patterns"},{"key":"10.1016\/j.displa.2026.103594_b119","series-title":"2023 30th National and 8th International Iranian Conference on Biomedical Engineering","first-page":"126","article-title":"VAE and AAE networks segment brain tumors from MRI","author":"Mirani","year":"2023"},{"key":"10.1016\/j.displa.2026.103594_b120","series-title":"An image is worth 16x16 words: Transformers for image recognition at scale","author":"Dosovitskiy","year":"2020"},{"issue":"2","key":"10.1016\/j.displa.2026.103594_b121","doi-asserted-by":"crossref","first-page":"1931","DOI":"10.1007\/s00521-022-07859-1","article-title":"D-former: A u-shaped dilated transformer for 3d medical image segmentation","volume":"35","author":"Wu","year":"2023","journal-title":"Neural Comput. Appl."},{"key":"10.1016\/j.displa.2026.103594_b122","doi-asserted-by":"crossref","DOI":"10.1016\/j.compbiomed.2025.109662","article-title":"VcaNet: Vision transformer with fusion channel and spatial attention module for 3D brain tumor segmentation","volume":"186","author":"Pan","year":"2025","journal-title":"Comput. Biol. Med."},{"issue":"4","key":"10.1016\/j.displa.2026.103594_b123","doi-asserted-by":"crossref","first-page":"3695","DOI":"10.1007\/s11063-022-10919-1","article-title":"BTSwin-U-Net: 3D U-shaped symmetrical swin transformer-based network for brain tumor segmentation with self-supervised pre-training","volume":"55","author":"Liang","year":"2023","journal-title":"Neural Process. Lett."},{"key":"10.1016\/j.displa.2026.103594_b124","doi-asserted-by":"crossref","DOI":"10.1016\/j.dsp.2022.103784","article-title":"3D pswinbts: an efficient transformer-based U-net using 3D parallel shifted windows for brain tumor segmentation","volume":"131","author":"Liang","year":"2022","journal-title":"Digit. Signal Process."},{"issue":"4","key":"10.1016\/j.displa.2026.103594_b125","doi-asserted-by":"crossref","first-page":"2397","DOI":"10.21037\/qims-21-919","article-title":"TransConver: transformer and convolution parallel network for developing automatic brain tumor segmentation in MRI images","volume":"12","author":"Liang","year":"2022","journal-title":"Quant. Imaging Med. Surg."},{"issue":"6","key":"10.1016\/j.displa.2026.103594_b126","doi-asserted-by":"crossref","first-page":"797","DOI":"10.3390\/brainsci12060797","article-title":"SwinBTS: A method for 3D multimodal brain tumor segmentation using swin transformer","volume":"12","author":"Jiang","year":"2022","journal-title":"Brain Sci."},{"key":"10.1016\/j.displa.2026.103594_b127","series-title":"International MICCAI Brainlesion Workshop","first-page":"3","article-title":"Bitr-unet: a cnn-transformer combined network for mri brain tumor segmentation","author":"Jia","year":"2021"},{"key":"10.1016\/j.displa.2026.103594_b128","doi-asserted-by":"crossref","first-page":"376","DOI":"10.1016\/j.inffus.2022.10.022","article-title":"Brain tumor segmentation based on the fusion of deep semantics and edge information in multimodal MRI","volume":"91","author":"Zhu","year":"2023","journal-title":"Inf. Fusion"},{"issue":"1","key":"10.1016\/j.displa.2026.103594_b129","doi-asserted-by":"crossref","DOI":"10.1002\/ima.22979","article-title":"Brain tumor image pixel segmentation and detection using an aggregation of GAN models with vision transformer","volume":"34","author":"Datta","year":"2024","journal-title":"Int. J. Imaging Syst. Technol."},{"issue":"2","key":"10.1016\/j.displa.2026.103594_b130","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1007\/s11548-023-03024-8","article-title":"Efficient brain tumor segmentation using swin transformer and enhanced local self-attention","volume":"19","author":"Ghazouani","year":"2024","journal-title":"Int. J. Comput. Assist. Radiol. Surg."},{"key":"10.1016\/j.displa.2026.103594_b131","series-title":"International MICCAI Brainlesion Workshop","first-page":"272","article-title":"Swin unetr: Swin transformers for semantic segmentation of brain tumors in mri images","author":"Hatamizadeh","year":"2021"},{"issue":"1","key":"10.1016\/j.displa.2026.103594_b132","article-title":"HybridCTrm: Bridging CNN and transformer for multimodal brain image segmentation","volume":"2021","author":"Sun","year":"2021","journal-title":"J. Heal. Eng."},{"key":"10.1016\/j.displa.2026.103594_b133","series-title":"International Symposium: From Data To Models and Back","first-page":"18","article-title":"Exploring graph-based neural networks for automatic brain tumor segmentation","author":"Saueressig","year":"2020"},{"key":"10.1016\/j.displa.2026.103594_b134","series-title":"International MICCAI Brainlesion Workshop","first-page":"140","article-title":"Predicting isocitrate dehydrogenase mutation status in glioma using structural brain networks and graph neural networks","author":"Wei","year":"2021"},{"key":"10.1016\/j.displa.2026.103594_b135","series-title":"2021 IEEE 4th International Conference on Big Data and Artificial Intelligence","first-page":"204","article-title":"Dual graph reasoning unit for brain tumor segmentation","author":"Ma","year":"2021"},{"key":"10.1016\/j.displa.2026.103594_b136","series-title":"International Conference on Computational Collective Intelligence","first-page":"337","article-title":"Hybrid architecture for 3D brain tumor image segmentation based on graph neural network pooling","author":"Gammoudi","year":"2022"},{"key":"10.1016\/j.displa.2026.103594_b137","series-title":"2023 8th International Conference on Signal and Image Processing","first-page":"196","article-title":"Multi-class brain tumor segmentation using graph attention network","author":"Patel","year":"2023"},{"key":"10.1016\/j.displa.2026.103594_b138","series-title":"International MICCAI Brainlesion Workshop","first-page":"356","article-title":"A joint graph and image convolution network for automatic brain tumor segmentation","author":"Saueressig","year":"2021"},{"issue":"8","key":"10.1016\/j.displa.2026.103594_b139","doi-asserted-by":"crossref","first-page":"3424","DOI":"10.3390\/app14083424","article-title":"Advancing brain tumor segmentation with spectral\u2013spatial graph neural networks","volume":"14","author":"Mohammadi","year":"2024","journal-title":"Appl. Sci."},{"issue":"1","key":"10.1016\/j.displa.2026.103594_b140","doi-asserted-by":"crossref","DOI":"10.1088\/2057-1976\/ada1db","article-title":"HeatGSNs: integrating eigenfilters and low-pass graph heat kernels into graph spectral convolutional networks for brain tumor segmentation and classification","volume":"11","author":"Bae","year":"2024","journal-title":"Biomed. Phys. Eng. Express"},{"key":"10.1016\/j.displa.2026.103594_b141","article-title":"M2GCNet: Multi-modal graph convolution network for precise brain tumor segmentation across multiple MRI sequences","author":"Zhou","year":"2024","journal-title":"IEEE Trans. Image Process."},{"key":"10.1016\/j.displa.2026.103594_b142","unstructured":"Bin Chen, Jiajun Wang, Zheru Chi, Improved DenseNet with convolutional attention module for brain tumor segmentation, in: Proceedings of the Third International Symposium on Image Computing and Digital Medicine, 2019, pp. 22\u201326."},{"issue":"11","key":"10.1016\/j.displa.2026.103594_b143","doi-asserted-by":"crossref","first-page":"5310","DOI":"10.1109\/JBHI.2021.3109301","article-title":"Self-supervised multi-modal hybrid fusion network for brain tumor segmentation","volume":"26","author":"Fang","year":"2021","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"10.1016\/j.displa.2026.103594_b144","series-title":"2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society","first-page":"5894","article-title":"Deep learning and multi-sensor fusion for glioma classification using multistream 2D convolutional networks","author":"Ge","year":"2018"},{"issue":"4","key":"10.1016\/j.displa.2026.103594_b145","article-title":"Deep autoencoder-decoder framework for semantic segmentation of brain tumor","volume":"15","author":"Naz","year":"2019","journal-title":"Aust. J. Intell. Inf. Process. Syst."},{"key":"10.1016\/j.displa.2026.103594_b146","doi-asserted-by":"crossref","DOI":"10.1109\/JBHI.2023.3321602","article-title":"WS-MTST: Weakly supervised multi-label brain tumor segmentation with transformers","author":"Chen","year":"2023","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"10.1016\/j.displa.2026.103594_b147","article-title":"TransSea: Hybrid CNN-transformer with semantic awareness for 3D brain tumor segmentation","author":"Liu","year":"2024","journal-title":"IEEE Trans. Instrum. Meas."},{"issue":"1","key":"10.1016\/j.displa.2026.103594_b148","article-title":"Augmented transformer network for MRI brain tumor segmentation","volume":"36","author":"Zhang","year":"2024","journal-title":"J. King Saud University-Computer Inf. Sci."},{"issue":"6","key":"10.1016\/j.displa.2026.103594_b149","doi-asserted-by":"crossref","first-page":"1550","DOI":"10.1049\/ipr2.13048","article-title":"Brain tumor segmentation framework with deep nuanced reasoning and Swin-T","volume":"18","author":"Xu","year":"2024","journal-title":"IET Image Process."},{"key":"10.1016\/j.displa.2026.103594_b150","first-page":"1","article-title":"Robust automated tumor segmentation network using 3D direction-wise convolution and transformer","author":"Chu","year":"2024","journal-title":"J. Imaging Informatics Med."},{"issue":"1","key":"10.1016\/j.displa.2026.103594_b151","doi-asserted-by":"crossref","first-page":"77","DOI":"10.3390\/electronics13010077","article-title":"RFTNet: Region\u2013attention fusion network combined with dual-branch vision transformer for multimodal brain tumor image segmentation","volume":"13","author":"Jiao","year":"2023","journal-title":"Electronics"},{"key":"10.1016\/j.displa.2026.103594_b152","doi-asserted-by":"crossref","DOI":"10.1016\/j.media.2022.102475","article-title":"Unsupervised brain imaging 3D anomaly detection and segmentation with transformers","volume":"79","author":"Pinaya","year":"2022","journal-title":"Med. Image Anal."},{"key":"10.1016\/j.displa.2026.103594_b153","doi-asserted-by":"crossref","DOI":"10.1016\/j.bspc.2023.105827","article-title":"MMMViT: Multiscale multimodal vision transformer for brain tumor segmentation with missing modalities","volume":"90","author":"Qiu","year":"2024","journal-title":"Biomed. Signal Process. Control."},{"key":"10.1016\/j.displa.2026.103594_b154","doi-asserted-by":"crossref","DOI":"10.3389\/fnins.2023.1192867","article-title":"Focal cross transformer: multi-view brain tumor segmentation model based on cross window and focal self-attention","volume":"17","author":"Zongren","year":"2023","journal-title":"Front. Neurosci."},{"issue":"4","key":"10.1016\/j.displa.2026.103594_b155","doi-asserted-by":"crossref","first-page":"2565","DOI":"10.1109\/TCBB.2022.3195705","article-title":"RLSegNet: A medical image segmentation network based on reinforcement learning","volume":"20","author":"Ding","year":"2022","journal-title":"IEEE\/ACM Trans. Comput. Biology Bioinform."},{"key":"10.1016\/j.displa.2026.103594_b156","series-title":"Deep reinforcement learning for fMRI prediction of autism spectrum disorder","author":"Stember","year":"2022"},{"issue":"11","key":"10.1016\/j.displa.2026.103594_b157","article-title":"Detecting brain diseases using hyper integral segmentation approach (HISA) and reinforcement learning","volume":"13","author":"Praveena","year":"2022","journal-title":"Int. J. Adv. Comput. Sci. Appl."},{"issue":"3","key":"10.1016\/j.displa.2026.103594_b158","article-title":"Influence of data distribution on federated learning performance in tumor segmentation","volume":"5","author":"Luo","year":"2023","journal-title":"Radiol.: Artif. Intell."},{"key":"10.1016\/j.displa.2026.103594_b159","doi-asserted-by":"crossref","DOI":"10.1016\/j.compbiomed.2024.108646","article-title":"Privacy-preserving blockchain-based federated learning for brain tumor segmentation","author":"Kumar","year":"2024","journal-title":"Comput. Biol. Med."},{"issue":"24","key":"10.1016\/j.displa.2026.103594_b160","doi-asserted-by":"crossref","first-page":"2891","DOI":"10.3390\/diagnostics14242891","article-title":"Federated learning with privacy preserving for multi-institutional three-dimensional brain tumor segmentation","volume":"14","author":"Yahiaoui","year":"2024","journal-title":"Diagnostics"},{"issue":"9","key":"10.1016\/j.displa.2026.103594_b161","doi-asserted-by":"crossref","first-page":"4635","DOI":"10.1109\/JBHI.2022.3185956","article-title":"Splitavg: A heterogeneity-aware federated deep learning method for medical imaging","volume":"26","author":"Zhang","year":"2022","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"10.1016\/j.displa.2026.103594_b162","series-title":"2024 6th International Conference on Pattern Analysis and Intelligent Systems","first-page":"1","article-title":"Federated learning for multi-institutional on 3D brain tumor segmentation","author":"Elbachir","year":"2024"},{"issue":"4","key":"10.1016\/j.displa.2026.103594_b163","doi-asserted-by":"crossref","first-page":"3779","DOI":"10.1007\/s11063-022-11014-1","article-title":"Effectiveness of federated learning and CNN ensemble architectures for identifying brain tumors using MRI images","volume":"55","author":"Islam","year":"2023","journal-title":"Neural Process. Lett."},{"issue":"9","key":"10.1016\/j.displa.2026.103594_b164","first-page":"2589","article-title":"IPC-CNN: A robust solution for precise brain tumor segmentation using improved privacy-preserving collaborative convolutional neural network","volume":"18","author":"Raheem","year":"2024","journal-title":"KSII Trans. Internet Inf. Syst. (TIIS)"},{"key":"10.1016\/j.displa.2026.103594_b165","first-page":"1445","article-title":"Federated modality-specific encoders and multimodal anchors for personalized brain tumor segmentation","volume":"vol. s38","author":"Dai","year":"2024"},{"key":"10.1016\/j.displa.2026.103594_b166","series-title":"Medical Imaging with Deep Learning","first-page":"957","article-title":"Whole brain radiomics for clustered federated personalization in brain tumor segmentation","author":"Manthe","year":"2024"},{"key":"10.1016\/j.displa.2026.103594_b167","series-title":"Differential privacy for adaptive weight aggregation in federated tumor segmentation","author":"Khan","year":"2023"},{"issue":"19","key":"10.1016\/j.displa.2026.103594_b168","doi-asserted-by":"crossref","first-page":"4189","DOI":"10.3390\/math11194189","article-title":"Enhancing brain tumor segmentation accuracy through scalable federated learning with advanced data privacy and security measures","volume":"11","author":"Ullah","year":"2023","journal-title":"Mathematics"},{"key":"10.1016\/j.displa.2026.103594_b169","series-title":"2025 IEEE 6th International Seminar on Artificial Intelligence, Networking and Information Technology","first-page":"624","article-title":"A lightweight 3D brain tumor segmentation network, dp-net, based on multi-scale feature extraction and prior guidance","author":"Liang","year":"2025"},{"key":"10.1016\/j.displa.2026.103594_b170","series-title":"2024 7th International Conference on Data Science and Information Technology","first-page":"1","article-title":"Balancing privacy and accuracy: Federated learning with differential privacy for medical image data","author":"Mehmood","year":"2024"},{"issue":"3","key":"10.1016\/j.displa.2026.103594_b171","doi-asserted-by":"crossref","first-page":"857","DOI":"10.1002\/ima.22677","article-title":"GAN-segNet: A deep generative adversarial segmentation network for brain tumor semantic segmentation","volume":"32","author":"Cui","year":"2022","journal-title":"Int. J. Imaging Syst. Technol."},{"key":"10.1016\/j.displa.2026.103594_b172","doi-asserted-by":"crossref","DOI":"10.1016\/j.bspc.2022.103537","article-title":"Optimal DeepMRSeg based tumor segmentation with GAN for brain tumor classification","volume":"74","author":"Neelima","year":"2022","journal-title":"Biomed. Signal Process. Control."},{"key":"10.1016\/j.displa.2026.103594_b173","doi-asserted-by":"crossref","DOI":"10.1016\/j.bspc.2021.103155","article-title":"A new 3D MRI segmentation method based on generative adversarial network and atrous convolution","volume":"71","author":"\u00c7elik","year":"2022","journal-title":"Biomed. Signal Process. Control."},{"issue":"1","key":"10.1016\/j.displa.2026.103594_b174","doi-asserted-by":"crossref","first-page":"119","DOI":"10.3390\/medicina59010119","article-title":"A novel generative adversarial network-based approach for automated brain tumour segmentation","volume":"59","author":"Sille","year":"2023","journal-title":"Medicina"},{"issue":"6","key":"10.1016\/j.displa.2026.103594_b175","first-page":"1","article-title":"3V3d: three-view contextual cross-slice difference three-dimensional medical image segmentation adversarial network","volume":"19","author":"Zeng","year":"2023","journal-title":"ACM Trans. Multimed. Comput. Commun. Appl."},{"key":"10.1016\/j.displa.2026.103594_b176","first-page":"1","article-title":"Prediction of prognosis in glioblastoma with radiomics features extracted by synthetic MRI images using cycle-consistent GAN","author":"Yoshimura","year":"2024","journal-title":"Phys. Eng. Sci. Med."},{"key":"10.1016\/j.displa.2026.103594_b177","doi-asserted-by":"crossref","DOI":"10.1016\/j.bspc.2024.106005","article-title":"Brain tumour segmentation and classification with reconstructed MRI using DCGAN","volume":"92","author":"Sandhiya","year":"2024","journal-title":"Biomed. Signal Process. Control."},{"key":"10.1016\/j.displa.2026.103594_b178","doi-asserted-by":"crossref","first-page":"100","DOI":"10.1016\/j.patrec.2024.11.003","article-title":"Segmentation of MRI tumors and pelvic anatomy via cGAN-synthesized data and attention-enhanced U-net","volume":"187","author":"Ali","year":"2025","journal-title":"Pattern Recognit. Lett."},{"key":"10.1016\/j.displa.2026.103594_b179","doi-asserted-by":"crossref","DOI":"10.1016\/j.compbiomed.2024.107982","article-title":"Medical image synthesis via conditional GANs: Application to segmenting brain tumours","volume":"170","author":"Hamghalam","year":"2024","journal-title":"Comput. Biol. Med."},{"issue":"4","key":"10.1016\/j.displa.2026.103594_b180","article-title":"Multi-objective image fusion for brain tumor detection using improved weighted quantum firefly optimization and StyleGAN-MAE-SwinViT.","volume":"42","author":"Nagarathinam","year":"2025","journal-title":"Trait. Du Signal"},{"key":"10.1016\/j.displa.2026.103594_b181","series-title":"2024 5th International Conference on Data Intelligence and Cognitive Informatics","first-page":"817","article-title":"StyleUNet: An enhanced style transfer for brain MRI images using StyleGAN with U-net","author":"Kumar","year":"2024"},{"issue":"1","key":"10.1016\/j.displa.2026.103594_b182","doi-asserted-by":"crossref","first-page":"29576","DOI":"10.1038\/s41598-024-78688-6","article-title":"Innovative multi-class segmentation for brain tumor MRI using noise diffusion probability models and enhancing tumor boundary recognition","volume":"14","author":"Liu","year":"2024","journal-title":"Sci. Rep."},{"key":"10.1016\/j.displa.2026.103594_b183","series-title":"2024 9th International Conference on Image, Vision and Computing","first-page":"188","article-title":"MBTDiff: Multi-segmentation brain tumor model with diffusion probabilistic model","author":"Zeng","year":"2024"},{"key":"10.1016\/j.displa.2026.103594_b184","series-title":"2024 International Joint Conference on Neural Networks","first-page":"1","article-title":"Two-stage diffusion model for 3D medical image segmentation","author":"Nishimura","year":"2024"},{"key":"10.1016\/j.displa.2026.103594_b185","doi-asserted-by":"crossref","DOI":"10.1109\/LSP.2024.3466608","article-title":"Edge-and-mask integration-driven diffusion models for medical image segmentation","author":"Tang","year":"2024","journal-title":"IEEE Signal Process. Lett."},{"key":"10.1016\/j.displa.2026.103594_b186","doi-asserted-by":"crossref","DOI":"10.24294\/mipt.v6i1.2518","article-title":"2D brain MRI image synthesis based on lightweight denoising diffusion probabilistic model","volume":"6","author":"Peng","year":"2023","journal-title":"Med. Imaging Process. Technol."},{"key":"10.1016\/j.displa.2026.103594_b187","doi-asserted-by":"crossref","DOI":"10.1016\/j.ijleo.2022.169474","article-title":"An efficient method for brain image preprocessing with anisotropic diffusion filter & tumor segmentation","volume":"265","author":"Maurya","year":"2022","journal-title":"Optik"},{"issue":"3","key":"10.1016\/j.displa.2026.103594_b188","doi-asserted-by":"crossref","first-page":"1587","DOI":"10.1109\/JBHI.2024.3353272","article-title":"CorrDiff: corrective diffusion model for accurate MRI brain tumor segmentation","volume":"28","author":"Li","year":"2024","journal-title":"IEEE J. Biomed. Health Inform."},{"issue":"1","key":"10.1016\/j.displa.2026.103594_b189","doi-asserted-by":"crossref","first-page":"224","DOI":"10.1186\/s12880-022-00919-x","article-title":"Reinforcement learning using deep Q networks and Q learning accurately localizes brain tumors on MRI with very small training sets","volume":"22","author":"Stember","year":"2022","journal-title":"BMC Med. Imaging"},{"key":"10.1016\/j.displa.2026.103594_b190","series-title":"2022 IEEE 4th International Conference on Cybernetics, Cognition and Machine Learning Applications","first-page":"302","article-title":"Optimized deep learning technique for brain tumor segmenting, contouring, and detection","author":"Banerjee","year":"2022"},{"issue":"4","key":"10.1016\/j.displa.2026.103594_b191","doi-asserted-by":"crossref","first-page":"1373","DOI":"10.11591\/ijai.v11.i4.pp1373-1383","article-title":"Brain tumor segmentation using double density dual tree complex wavelet transform combined with convolutional neural network and genetic algorithm","volume":"11","author":"Samosir","year":"2022","journal-title":"IAES Int. J. Artif. Intell."},{"key":"10.1016\/j.displa.2026.103594_b192","article-title":"Deep generative adversarial reinforcement learning for semi-supervised segmentation of low-contrast and small objects in medical images","author":"Xu","year":"2024","journal-title":"IEEE Trans. Med. Imaging"},{"key":"10.1016\/j.displa.2026.103594_b193","first-page":"1","article-title":"Active learning in brain tumor segmentation with uncertainty sampling and annotation redundancy restriction","author":"Kim","year":"2024","journal-title":"J. Imaging Informatics Med."},{"key":"10.1016\/j.displa.2026.103594_b194","article-title":"Federated learning for brain tumor segmentation","author":"Evaldsson","year":"2024","journal-title":"J. Name"},{"key":"10.1016\/j.displa.2026.103594_b195","series-title":"Fed-MU-net: Multi-modal federated U-net for brain tumor segmentation","author":"Zhou","year":"2024"},{"key":"10.1016\/j.displa.2026.103594_b196","doi-asserted-by":"crossref","DOI":"10.1016\/j.compbiomed.2024.108585","article-title":"Model-data-driven adversarial active learning for brain tumor segmentation","volume":"176","author":"Ma","year":"2024","journal-title":"Comput. Biol. Med."},{"key":"10.1016\/j.displa.2026.103594_b197","doi-asserted-by":"crossref","DOI":"10.3389\/fnins.2023.1043533","article-title":"Dual adversarial models with cross-coordination consistency constraint for domain adaption in brain tumor segmentation","volume":"17","author":"Qin","year":"2023","journal-title":"Front. Neurosci."},{"issue":"1","key":"10.1016\/j.displa.2026.103594_b198","doi-asserted-by":"crossref","first-page":"259","DOI":"10.1038\/s41597-024-03073-x","article-title":"Brain tumor segmentation using synthetic MR images-a comparison of GANs and diffusion models","volume":"11","author":"Usman Akbar","year":"2024","journal-title":"Sci. Data"},{"issue":"6","key":"10.1016\/j.displa.2026.103594_b199","doi-asserted-by":"crossref","first-page":"342","DOI":"10.1007\/s00530-024-01580-w","article-title":"EDB-diff: a EdgeDevice based diffusion network for brain tumor image segmentation","volume":"30","author":"Liu","year":"2024","journal-title":"Multimedia Syst."},{"issue":"12","key":"10.1016\/j.displa.2026.103594_b200","doi-asserted-by":"crossref","first-page":"3289","DOI":"10.1007\/s11517-023-02899-8","article-title":"Uncertainty-guided transformer for brain tumor segmentation","volume":"61","author":"Chen","year":"2023","journal-title":"Med. Biol. Eng. Comput."},{"key":"10.1016\/j.displa.2026.103594_b201","unstructured":"Y. Qiu, Z. Zhao, H. Yao, D. Chen, Z. Wang, Modal-aware visual prompting for incomplete multi-modal brain tumor segmentation, in: Proceedings of the 31st ACM International Conference on Multimedia, 2023, pp. 3228\u20133239."},{"key":"10.1016\/j.displa.2026.103594_b202","doi-asserted-by":"crossref","DOI":"10.1016\/j.neucom.2024.128691","article-title":"FedATA: Adaptive attention aggregation for federated self-supervised medical image segmentation","volume":"613","author":"Dai","year":"2025","journal-title":"Neurocomputing"},{"issue":"1","key":"10.1016\/j.displa.2026.103594_b203","doi-asserted-by":"crossref","first-page":"196","DOI":"10.1007\/s44196-024-00620-7","article-title":"Self-supervised contrastive learning for automated segmentation of brain tumor MRI images in schizophrenia","volume":"17","author":"Meng","year":"2024","journal-title":"Int. J. Comput. Intell. Syst."},{"key":"10.1016\/j.displa.2026.103594_b204","doi-asserted-by":"crossref","DOI":"10.1016\/j.bspc.2024.106343","article-title":"Intra-modality masked image modeling: A self-supervised pre-training method for brain tumor segmentation","volume":"95","author":"Qi","year":"2024","journal-title":"Biomed. Signal Process. Control."},{"issue":"3","key":"10.1016\/j.displa.2026.103594_b205","doi-asserted-by":"crossref","first-page":"249","DOI":"10.3390\/diagnostics15030249","article-title":"Feasibility study of detecting and segmenting small brain tumors in a small MRI dataset with self-supervised learning","volume":"15","author":"Zhang","year":"2025","journal-title":"Diagnostics"},{"key":"10.1016\/j.displa.2026.103594_b206","doi-asserted-by":"crossref","DOI":"10.1109\/JBHI.2025.3530715","article-title":"Online self-distillation and self-modeling for 3D brain tumor segmentation","author":"Pang","year":"2025","journal-title":"IEEE J. Biomed. Health Informatics"},{"key":"10.1016\/j.displa.2026.103594_b207","doi-asserted-by":"crossref","DOI":"10.1016\/j.inffus.2025.103301","article-title":"Multi-view information fusion based on federated multi-objective neural architecture search for MRI semantic segmentation","author":"Cao","year":"2025","journal-title":"Inf. Fusion"},{"key":"10.1016\/j.displa.2026.103594_b208","series-title":"Enhancing brain tumor segmentation using channel attention and transfer learning","author":"Behzadpour","year":"2025"},{"issue":"5","key":"10.1016\/j.displa.2026.103594_b209","doi-asserted-by":"crossref","first-page":"2509","DOI":"10.1007\/s11042-024-20353-1","article-title":"Brain tumor segmentation and classification using transfer learning based CNN model with model agnostic concept interpretation","volume":"84","author":"Nancy","year":"2025","journal-title":"Multimedia Tools Appl."},{"key":"10.1016\/j.displa.2026.103594_b210","first-page":"14","article-title":"Brain tumor classification and segmentation using transfer learning from MRI images","volume":"17","author":"Madhavi","year":"2025","journal-title":"Int. J. Comput. Inf. Syst. Ind. Manag. Appl."},{"key":"10.1016\/j.displa.2026.103594_b211","series-title":"2024 OPJU International Technology Conference (OTCON) on Smart Computing for Innovation and Advancement in Industry 4.0","first-page":"1","article-title":"Image segmentation for mri brain tumor detection using advance ai algorithm","author":"Karthikeyan","year":"2024"},{"issue":"21","key":"10.1016\/j.displa.2026.103594_b212","doi-asserted-by":"crossref","first-page":"7091","DOI":"10.3390\/s24217091","article-title":"Edge computing for AI-based brain MRI applications: A critical evaluation of real-time classification and segmentation","volume":"24","author":"Memon","year":"2024","journal-title":"Sensors"},{"key":"10.1016\/j.displa.2026.103594_b213","doi-asserted-by":"crossref","DOI":"10.7717\/peerj-cs.1754","article-title":"Brain tumor segmentation using U-net in conjunction with EfficientNet","volume":"10","author":"Lin","year":"2024","journal-title":"PeerJ Comput. Sci."},{"key":"10.1016\/j.displa.2026.103594_b214","doi-asserted-by":"crossref","DOI":"10.1016\/j.compbiomed.2025.110460","article-title":"FFLUNet: Feature fused lightweight unet for brain tumor segmentation","volume":"194","author":"Kundu","year":"2025","journal-title":"Comput. Biol. Med."},{"issue":"1","key":"10.1016\/j.displa.2026.103594_b215","first-page":"1","article-title":"Brain tumor segmentation using multi-scale attention U-net with EfficientNetB4 encoder for enhanced MRI analysis","volume":"15","author":"JS","year":"2025","journal-title":"Sci. Rep."},{"issue":"5","key":"10.1016\/j.displa.2026.103594_b216","doi-asserted-by":"crossref","first-page":"528","DOI":"10.1007\/s42979-025-03900-x","article-title":"Deep learning framework for breast cancer detection and segmentation using EfficientNet and U-net with hyperparameter optimization","volume":"6","author":"Abdel-Wahab","year":"2025","journal-title":"SN Comput. Sci."},{"issue":"4","key":"10.1016\/j.displa.2026.103594_b217","doi-asserted-by":"crossref","first-page":"220","DOI":"10.1007\/s00432-024-05718-1","article-title":"Two-headed UNetEfficientNets for parallel execution of segmentation and classification of brain tumors: Incorporating postprocessing techniques with connected component labelling","volume":"150","author":"Rai","year":"2024","journal-title":"J. Cancer Res. Clin. Oncol."},{"key":"10.1016\/j.displa.2026.103594_b218","series-title":"2024 IEEE 3rd World Conference on Applied Intelligence and Computing","first-page":"777","article-title":"An effective brain tumor segmentation and classification framework using transformer-based Res-Unet++ and ShuffleNetV2","author":"Kumar","year":"2024"},{"key":"10.1016\/j.displa.2026.103594_b219","doi-asserted-by":"crossref","DOI":"10.1016\/j.compbiomed.2024.108005","article-title":"ETUNet: Exploring efficient transformer enhanced unet for 3D brain tumor segmentation","volume":"171","author":"Zhang","year":"2024","journal-title":"Comput. Biol. Med."},{"issue":"4","key":"10.1016\/j.displa.2026.103594_b220","doi-asserted-by":"crossref","first-page":"1287","DOI":"10.1007\/s12559-022-10038-y","article-title":"FF-UNet: a U-shaped deep convolutional neural network for multimodal biomedical image segmentation","volume":"14","author":"Iqbal","year":"2022","journal-title":"Cogn. Comput."},{"key":"10.1016\/j.displa.2026.103594_b221","series-title":"Medical Imaging with Deep Learning","first-page":"799","article-title":"3D medical axial transformer: a lightweight transformer model for 3D brain tumor segmentation","author":"Liu","year":"2024"},{"key":"10.1016\/j.displa.2026.103594_b222","doi-asserted-by":"crossref","first-page":"152430","DOI":"10.1109\/ACCESS.2024.3480271","article-title":"EffUNet++: a novel architecture for brain tumor segmentation using FLAIR MRI images","volume":"12","author":"Yadav","year":"2024","journal-title":"IEEE Access"},{"issue":"1","key":"10.1016\/j.displa.2026.103594_b223","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1007\/s12559-024-10387-w","article-title":"A novel interpretable graph convolutional neural network for multimodal brain tumor segmentation","volume":"17","author":"Arshad Choudhry","year":"2025","journal-title":"Cogn. Comput."},{"key":"10.1016\/j.displa.2026.103594_b224","series-title":"2024 34th International Conference on Computer Theory and Applications","first-page":"283","article-title":"An explainable approach for brain tumor segmentation using Grad-CAM based U-net models","author":"Abd-Elhafeez","year":"2024"},{"key":"10.1016\/j.displa.2026.103594_b225","first-page":"1","article-title":"Brain tumor segmentation based on deep learning, attention mechanisms, and energy-based uncertainty predictions","author":"Schwehr","year":"2025","journal-title":"Multimedia Tools Appl."},{"key":"10.1016\/j.displa.2026.103594_b226","article-title":"BrainView: A cloud-based deep learning system for brain image segmentation, tumor detection and visualization","author":"Ghose","year":"2025","journal-title":"Biomed. J."},{"issue":"8","key":"10.1016\/j.displa.2026.103594_b227","doi-asserted-by":"crossref","first-page":"2451","DOI":"10.1109\/TMI.2023.3250474","article-title":"CKD-transbts: clinical knowledge-driven hybrid transformer with modality-correlated cross-attention for brain tumor segmentation","volume":"42","author":"Lin","year":"2023","journal-title":"IEEE Trans. Med. Imaging"},{"key":"10.1016\/j.displa.2026.103594_b228","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1016\/j.media.2016.05.004","article-title":"Brain tumor segmentation with deep neural networks","volume":"35","author":"Havaei","year":"2017","journal-title":"Med. Image Anal."},{"issue":"7","key":"10.1016\/j.displa.2026.103594_b229","doi-asserted-by":"crossref","first-page":"427","DOI":"10.1007\/s10586-025-05110-9","article-title":"A robust U-net-based cascaded model for brain tumor substructures segmentation using magnetic resonance imaging","volume":"28","author":"Ali","year":"2025","journal-title":"Clust. Comput."},{"key":"10.1016\/j.displa.2026.103594_b230","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1016\/j.media.2016.10.004","article-title":"Efficient multi-scale 3D CNN with fully connected CRF for accurate brain lesion segmentation","volume":"36","author":"Kamnitsas","year":"2017","journal-title":"Med. Image Anal."},{"issue":"8","key":"10.1016\/j.displa.2026.103594_b231","doi-asserted-by":"crossref","first-page":"1253","DOI":"10.1007\/s00234-021-02649-3","article-title":"Application of deep learning for automatic segmentation of brain tumors on magnetic resonance imaging: a heuristic approach in the clinical scenario","volume":"63","author":"Di Ieva","year":"2021","journal-title":"Neuroradiology"},{"issue":"17","key":"10.1016\/j.displa.2026.103594_b232","doi-asserted-by":"crossref","first-page":"4952","DOI":"10.1002\/hbm.24750","article-title":"Automated brain extraction of multisequence MRI using artificial neural networks","volume":"40","author":"Isensee","year":"2019","journal-title":"Hum. Brain Mapp."},{"issue":"1","key":"10.1016\/j.displa.2026.103594_b233","doi-asserted-by":"crossref","first-page":"18911","DOI":"10.1038\/s41598-023-44794-0","article-title":"Multi-class glioma segmentation on real-world data with missing MRI sequences: comparison of three deep learning algorithms","volume":"13","author":"Pemberton","year":"2023","journal-title":"Sci. Rep."},{"key":"10.1016\/j.displa.2026.103594_b234","doi-asserted-by":"crossref","first-page":"125","DOI":"10.3389\/fnins.2020.00125","article-title":"Brats toolkit: translating brats brain tumor segmentation algorithms into clinical and scientific practice","volume":"14","author":"Kofler","year":"2020","journal-title":"Front. Neurosci."},{"key":"10.1016\/j.displa.2026.103594_b235","doi-asserted-by":"crossref","DOI":"10.3389\/fneur.2022.932219","article-title":"Preoperative brain tumor imaging: models and software for segmentation and standardized reporting","volume":"13","author":"Bouget","year":"2022","journal-title":"Front. Neurol."},{"key":"10.1016\/j.displa.2026.103594_b236","series-title":"International Conference on Medical Image Computing and Computer-Assisted Intervention","first-page":"184","article-title":"3D dilated multi-fiber network for real-time brain tumor segmentation in MRI","author":"Chen","year":"2019"},{"issue":"37","key":"10.1016\/j.displa.2026.103594_b237","doi-asserted-by":"crossref","first-page":"85027","DOI":"10.1007\/s11042-024-19406-2","article-title":"An improved 3D U-net-based deep learning system for brain tumor segmentation using multi-modal MRI","volume":"83","author":"Ali","year":"2024","journal-title":"Multimedia Tools Appl."},{"issue":"11","key":"10.1016\/j.displa.2026.103594_b238","doi-asserted-by":"crossref","first-page":"5526","DOI":"10.1016\/j.eswa.2014.01.021","article-title":"Computer-aided diagnosis of human brain tumor through MRI: A survey and a new algorithm","volume":"41","author":"El-Dahshan","year":"2014","journal-title":"Expert Syst. Appl."},{"key":"10.1016\/j.displa.2026.103594_b239","doi-asserted-by":"crossref","DOI":"10.1016\/j.compmedimag.2022.102167","article-title":"A literature survey of MR-based brain tumor segmentation with missing modalities","volume":"104","author":"Zhou","year":"2023","journal-title":"Comput. Med. Imaging Graph."},{"key":"10.1016\/j.displa.2026.103594_b240","doi-asserted-by":"crossref","DOI":"10.1016\/j.compbiomed.2022.106405","article-title":"Brain tumor segmentation of MRI images: A comprehensive review on the application of artificial intelligence tools","volume":"152","author":"Ranjbarzadeh","year":"2023","journal-title":"Comput. Biol. Med."},{"key":"10.1016\/j.displa.2026.103594_b241","doi-asserted-by":"crossref","DOI":"10.1016\/j.compmedimag.2023.102313","article-title":"A review on brain tumor segmentation based on deep learning methods with federated learning techniques","author":"Ahamed","year":"2023","journal-title":"Comput. Med. Imaging Graph."},{"issue":"6","key":"10.1016\/j.displa.2026.103594_b242","doi-asserted-by":"crossref","first-page":"521","DOI":"10.1016\/j.irbm.2022.05.002","article-title":"A review on convolutional neural networks for brain tumor segmentation: methods, datasets, libraries, and future directions","volume":"43","author":"Balwant","year":"2022","journal-title":"IRBM"},{"key":"10.1016\/j.displa.2026.103594_b243","doi-asserted-by":"crossref","first-page":"244","DOI":"10.1016\/j.patrec.2019.11.020","article-title":"Brain tumor segmentation and classification from magnetic resonance images: Review of selected methods from 2014 to 2019","volume":"131","author":"Tiwari","year":"2020","journal-title":"Pattern Recognit. Lett."},{"issue":"2","key":"10.1016\/j.displa.2026.103594_b244","doi-asserted-by":"crossref","first-page":"181","DOI":"10.1007\/s12553-023-00737-3","article-title":"A survey of deep learning for MRI brain tumor segmentation methods: Trends, challenges, and future directions","volume":"13","author":"Krishnapriya","year":"2023","journal-title":"Health Technol."},{"issue":"10","key":"10.1016\/j.displa.2026.103594_b245","doi-asserted-by":"crossref","first-page":"30505","DOI":"10.1007\/s11042-023-16654-6","article-title":"Advancements in hybrid approaches for brain tumor segmentation in MRI: a comprehensive review of machine learning and deep learning techniques","volume":"83","author":"Sajjanar","year":"2024","journal-title":"Multimedia Tools Appl."},{"key":"10.1016\/j.displa.2026.103594_b246","first-page":"1","article-title":"Brain tumour detection using machine and deep learning: a systematic review","author":"Rasool","year":"2024","journal-title":"Multimedia Tools Appl."},{"issue":"2","key":"10.1016\/j.displa.2026.103594_b247","doi-asserted-by":"crossref","first-page":"275","DOI":"10.3390\/jpm12020275","article-title":"A comprehensive analysis of recent deep and federated-learning-based methodologies for brain tumor diagnosis","volume":"12","author":"Naeem","year":"2022","journal-title":"J. Pers. Med."},{"key":"10.1016\/j.displa.2026.103594_b248","doi-asserted-by":"crossref","first-page":"112117","DOI":"10.1109\/ACCESS.2022.3216393","article-title":"Data complexity based evaluation of the model dependence of brain MRI images for classification of brain tumor and alzheimer\u2019s disease","volume":"10","author":"Kujur","year":"2022","journal-title":"IEEE Access"},{"issue":"1","key":"10.1016\/j.displa.2026.103594_b249","article-title":"Enhanced U-net with attention mechanisms for improved feature representation in lung nodule segmentation","volume":"21","author":"Aung","year":"2025","journal-title":"Curr. Med. Imaging"},{"issue":"2","key":"10.1016\/j.displa.2026.103594_b250","doi-asserted-by":"crossref","first-page":"79","DOI":"10.26599\/IJCS.2024.9100007","article-title":"B5G applications and emerging services in smart IoT environments","volume":"9","author":"Attar","year":"2025","journal-title":"Int. J. Crowd Sci."},{"issue":"1","key":"10.1016\/j.displa.2026.103594_b251","doi-asserted-by":"crossref","first-page":"44","DOI":"10.26599\/IJCS.2023.9100033","article-title":"Enhancing organizational performance: Synergy of cyber-physical systems, cloud services, and crowdsensing","volume":"9","author":"Al-Qerem","year":"2025","journal-title":"Int. J. Crowd Sci."}],"container-title":["Displays"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S014193822600257X?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S014193822600257X?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,7,6]],"date-time":"2026-07-06T08:15:26Z","timestamp":1783325726000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S014193822600257X"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,12]]},"references-count":251,"alternative-id":["S014193822600257X"],"URL":"https:\/\/doi.org\/10.1016\/j.displa.2026.103594","relation":{},"ISSN":["0141-9382"],"issn-type":[{"value":"0141-9382","type":"print"}],"subject":[],"published":{"date-parts":[[2026,12]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Evolution of brain tumor segmentation: A multimodal imaging analysis from machine learning to emerging trends (2014\u20132025)","name":"articletitle","label":"Article Title"},{"value":"Displays","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.displa.2026.103594","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"103594"}}