{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,8]],"date-time":"2026-05-08T20:11:45Z","timestamp":1778271105170,"version":"3.51.4"},"reference-count":50,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62503276"],"award-info":[{"award-number":["62503276"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62502271"],"award-info":[{"award-number":["62502271"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100007129","name":"Natural Science Foundation of Shandong Province","doi-asserted-by":"publisher","award":["ZR2025QC630"],"award-info":[{"award-number":["ZR2025QC630"]}],"id":[{"id":"10.13039\/501100007129","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100007129","name":"Natural Science Foundation of Shandong Province","doi-asserted-by":"publisher","award":["ZR2025QC670"],"award-info":[{"award-number":["ZR2025QC670"]}],"id":[{"id":"10.13039\/501100007129","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100010909","name":"Excellent Young Scientists Fund","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100010909","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Information Sciences"],"published-print":{"date-parts":[[2026,8]]},"DOI":"10.1016\/j.ins.2026.123423","type":"journal-article","created":{"date-parts":[[2026,3,27]],"date-time":"2026-03-27T08:03:33Z","timestamp":1774598613000},"page":"123423","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["TumorNet: A hybrid lightweight framework for brain tumor classification and reasoning"],"prefix":"10.1016","volume":"746","author":[{"given":"Hanxiang","family":"Wang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Muhammad","family":"Zaqeem","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Muhammad","family":"Fayaz","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Defu","family":"Qiu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6586-2299","authenticated-orcid":false,"given":"Sajjad","family":"Ahadzadeh","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tan N.","family":"Nguyen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"L.Minh","family":"Dang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"78","reference":[{"issue":"Supplement 3","key":"10.1016\/j.ins.2026.123423_bib0005","article-title":"Primary brain and other central nervous system tumors diagnosed in the United States in 2014\u20132018","volume":"23","author":"Ostrom","year":"2021","journal-title":"Neuro-Oncology"},{"issue":"3","key":"10.1016\/j.ins.2026.123423_bib0010","first-page":"345","article-title":"Magnetic resonance imaging of brain tumors: a review","volume":"24","author":"Bauer","year":"2022","journal-title":"Neuro-Oncology"},{"key":"10.1016\/j.ins.2026.123423_bib0015","first-page":"135","article-title":"The multimodal brain tumor image segmentation benchmark","volume":"25","author":"Menze","year":"2021","journal-title":"Med. Image Anal."},{"key":"10.1016\/j.ins.2026.123423_bib0020","article-title":"Magnetic resonance imaging-based brain tumor classification using machine learning","volume":"104","author":"Anaraki","year":"2020","journal-title":"Artif. Intell. Med."},{"key":"10.1016\/j.ins.2026.123423_bib0025","first-page":"278","article-title":"Deep learning for brain MRI analysis","volume":"35","author":"Akkus","year":"2022","journal-title":"J. Digit. Imaging"},{"issue":"4","key":"10.1016\/j.ins.2026.123423_bib0030","first-page":"1711","article-title":"Non-local neural networks","volume":"44","author":"Wang","year":"2022","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"10.1016\/j.ins.2026.123423_bib0035","series-title":"International Conference on Learning Representation","article-title":"An image is worth 16x16 words: transformers for image recognition at scale","author":"Dosovitskiy","year":"2021"},{"key":"10.1016\/j.ins.2026.123423_bib0040","first-page":"45","article-title":"Efficient deep learning models for medical image analysis","volume":"16","author":"Chen","year":"2023","journal-title":"IEEE Rev. Biomed. Eng."},{"key":"10.1016\/j.ins.2026.123423_bib0045","article-title":"Hybrid CNN\u2013transformer architectures for medical image analysis","volume":"86","author":"Hatamizadeh","year":"2024","journal-title":"Med. Image Anal."},{"key":"10.1016\/j.ins.2026.123423_bib0050","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1016\/j.media.2017.07.005","article-title":"A survey on deep learning in medical image analysis","volume":"42","author":"Litjens","year":"2017","journal-title":"Med. Image Anal."},{"issue":"4","key":"10.1016\/j.ins.2026.123423_bib0055","doi-asserted-by":"crossref","first-page":"449","DOI":"10.1007\/s10278-017-9983-4","article-title":"Deep learning for brain MRI segmentation: state of the art and future directions","volume":"30","author":"Akkus","year":"2017","journal-title":"J. Digit. Imaging"},{"key":"10.1016\/j.ins.2026.123423_bib0060","first-page":"574","article-title":"Unetr: transformers for 3D medical image segmentation","author":"Hatamizadeh","year":"2022","journal-title":"Proc. IEEE\/CVF Winter Conf. Appl. Comput. Vis."},{"issue":"8","key":"10.1016\/j.ins.2026.123423_bib0065","doi-asserted-by":"crossref","first-page":"818","DOI":"10.3390\/bioengineering12080818","article-title":"Large language models in medical image analysis: a systematic survey and future directions","volume":"12","author":"Urooj","year":"2025","journal-title":"Bioengineering"},{"key":"10.1016\/j.ins.2026.123423_bib0070","first-page":"45","article-title":"Handcrafted features versus deep features in brain tumor classification","volume":"120","author":"Rao","year":"2019","journal-title":"Pattern Recognit. Lett."},{"key":"10.1016\/j.ins.2026.123423_bib0075","article-title":"Fine-tuned resnet50 for glioma grading using brats-2019 MRI dataset","volume":"93","author":"Mehmood","year":"2024","journal-title":"Biomed. Signal Process. Control"},{"key":"10.1016\/j.ins.2026.123423_bib0080","article-title":"Hybrid CNN-SVM model for glioma classification using MRI","volume":"164","author":"Shargunam","year":"2024","journal-title":"Comput. Biol. Med."},{"key":"10.1016\/j.ins.2026.123423_bib0085","article-title":"Cascaded CNN with u-net symmetry for brain tumor MRI classification","volume":"235","author":"Abd-Ellah","year":"2024","journal-title":"Expert Syst. Appl."},{"key":"10.1016\/j.ins.2026.123423_bib0090","first-page":"58522","article-title":"Attention-augmented resnet for brain tumor MRI classification","volume":"12","author":"Li","year":"2024","journal-title":"IEEE Access"},{"issue":"7","key":"10.1016\/j.ins.2026.123423_bib0095","first-page":"1245","article-title":"Resnet50 with bigru and dual attention for multi-class brain tumor classification","volume":"36","author":"Sreedevi","year":"2024","journal-title":"J. King Saud Univ. Comput. Inf. Sci."},{"key":"10.1016\/j.ins.2026.123423_bib0100","article-title":"Fusion of densenet and resnet features for brain tumor MRI classification","volume":"145","author":"Chen","year":"2024","journal-title":"Pattern Recognit."},{"key":"10.1016\/j.ins.2026.123423_bib0105","article-title":"Belief network-based fusion of resnet50 and vgg16 features for brain tumor classification","volume":"233","author":"Zebari","year":"2024","journal-title":"Expert Syst. Appl."},{"key":"10.1016\/j.ins.2026.123423_bib0110","article-title":"Modified VGG19 architecture for brain tumor MRI classification","volume":"94","author":"Haque","year":"2024","journal-title":"Biomed. Signal Process. Control"},{"key":"10.1016\/j.ins.2026.123423_bib0115","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.mri.2024.06.001","article-title":"Transformer-based architecture for differentiating glioblastoma and CNS lymphoma from MRI","volume":"105","author":"Wang","year":"2024","journal-title":"Magn. Reson. Imaging"},{"key":"10.1016\/j.ins.2026.123423_bib0120","first-page":"1","article-title":"Hybrid CNN-transformer architectures for MRI classification: balancing accuracy and efficiency","volume":"28","author":"Patel","year":"2024","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"10.1016\/j.ins.2026.123423_bib0125","article-title":"Magnetic resonance imaging-based brain tumor classification using deep learning techniques","volume":"57","author":"Anaraki","year":"2019","journal-title":"Biomed. Signal Process. Control"},{"issue":"6","key":"10.1016\/j.ins.2026.123423_bib0130","doi-asserted-by":"crossref","first-page":"1609","DOI":"10.1002\/mrm.22147","article-title":"Classification of brain tumor type and grade using MRI texture and shape features","volume":"62","author":"Zacharaki","year":"2009","journal-title":"Magn. Reson. Med."},{"issue":"7","key":"10.1016\/j.ins.2026.123423_bib0135","first-page":"1","article-title":"Brain tumor classification using handcrafted features and machine learning techniques","volume":"43","author":"Rao","year":"2019","journal-title":"J. Med. Syst."},{"key":"10.1016\/j.ins.2026.123423_bib0140","article-title":"Performance analysis of SVM-based brain tumor classification","volume":"123","author":"Alam","year":"2020","journal-title":"Comput. Biol. Med."},{"key":"10.1016\/j.ins.2026.123423_bib0145","article-title":"Traditional machine learning methods for brain tumor analysis: a review","volume":"155","author":"Sultana","year":"2023","journal-title":"Comput. Biol. Med."},{"key":"10.1016\/j.ins.2026.123423_bib0150","article-title":"Limitations of convolutional networks in modelling global context for medical images","volume":"72","author":"Nguyen","year":"2022","journal-title":"IEEE Trans. Med. Imaging"},{"key":"10.1016\/j.ins.2026.123423_bib0155","article-title":"Convolutional neural networks in medical image analysis: a comprehensive review","volume":"85","author":"Cheng","year":"2023","journal-title":"Artif. Intell. Rev."},{"key":"10.1016\/j.ins.2026.123423_bib0160","series-title":"Brain tumor MRI dataset","author":"Nick Parvar","year":"2022"},{"key":"10.1016\/j.ins.2026.123423_bib0165","series-title":"MRI for brain tumor with bounding boxes","author":"Sorour","year":"2023"},{"key":"10.1016\/j.ins.2026.123423_bib0170","author":"Rostami"},{"issue":"15","key":"10.1016\/j.ins.2026.123423_bib0175","first-page":"2533","article-title":"Brain tumor classification from MRI images using optimized densenet121 and inceptionv3","volume":"13","author":"Hossain","year":"2023","journal-title":"Diagnostics"},{"key":"10.1016\/j.ins.2026.123423_bib0180","article-title":"A resnet-based convolutional neural network for efficient brain tumor detection from MRI scans","volume":"240","author":"Kumar","year":"2023","journal-title":"Comput. Methods Programs Biomed."},{"key":"10.1016\/j.ins.2026.123423_bib0185","article-title":"Efficientnet-based hybrid transfer learning model for brain tumor classification","volume":"228","author":"Swati","year":"2023","journal-title":"Expert Syst. Appl."},{"issue":"9","key":"10.1016\/j.ins.2026.123423_bib0190","first-page":"14127","article-title":"Inceptionv3 feature fusion for MRI-based brain tumor recognition","volume":"82","author":"Ghassemi","year":"2023","journal-title":"Multim. Tools Appl."},{"key":"10.1016\/j.ins.2026.123423_bib0195","article-title":"Deep resnext architecture for multiclass brain tumor MRI classification","volume":"164","author":"Alanazi","year":"2023","journal-title":"J. Biomed. Inform."},{"key":"10.1016\/j.ins.2026.123423_bib0200","article-title":"DenseNet169-based multi-class brain tumor classification with MRI imaging","volume":"113","author":"Patel","year":"2024","journal-title":"Comput. Electr. Eng."},{"key":"10.1016\/j.ins.2026.123423_bib0205","article-title":"Regnety-based deep neural network for high-performance brain tumor MRI classification","volume":"73","author":"Tariq","year":"2024","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"10.1016\/j.ins.2026.123423_bib0210","first-page":"59983","article-title":"ResNet152 with CBAM attention for enhanced brain tumor classification","volume":"12","author":"Singh","year":"2024","journal-title":"IEEE Access"},{"key":"10.1016\/j.ins.2026.123423_bib0215","article-title":"EfficientNetV2-driven deep hybrid network for high-precision brain tumor MRI classification","volume":"153","author":"Kar","year":"2025","journal-title":"Artif. Intell. Med."},{"key":"10.1016\/j.ins.2026.123423_bib0220","first-page":"18971","article-title":"Convnext-based deep visual recognition for brain tumor MRI classification","volume":"35","author":"Wang","year":"2023","journal-title":"Neural Comput. Appl."},{"key":"10.1016\/j.ins.2026.123423_bib0225","article-title":"A novel hybrid deep learning model based on local and global feature fusion for brain tumor classification using MRI images","volume":"99","author":"Pacal","year":"2025","journal-title":"Biomed. Eng. Lett."},{"key":"10.1016\/j.ins.2026.123423_bib0230","article-title":"Mobdensenet: densenet201-based hybrid architecture for high-accuracy brain tumor MRI classification","volume":"136","author":"Afroj","year":"2025","journal-title":"Pattern Recognit."},{"key":"10.1016\/j.ins.2026.123423_bib0235","first-page":"55","article-title":"Vision transformer for brain tumor MRI classification: a comparative analysis","volume":"172","author":"Luo","year":"2023","journal-title":"Pattern Recognit. Lett."},{"key":"10.1016\/j.ins.2026.123423_bib0240","article-title":"Transformer-based hybrid learning model for brain tumor classification using MRI","volume":"172","author":"Zhang","year":"2024","journal-title":"Neural Netw."},{"key":"10.1016\/j.ins.2026.123423_bib0245","article-title":"Hybrid CNN\u2013vit framework for lightweight and accurate brain tumor detection","volume":"169","author":"Hanif","year":"2024","journal-title":"Comput. Biol. Med."},{"key":"10.1016\/j.ins.2026.123423_bib0250","first-page":"45421","article-title":"Attention-guided densenet-169 for robust brain tumor MRI classification","volume":"12","author":"Ahmed","year":"2024","journal-title":"IEEE Access"}],"container-title":["Information Sciences"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0020025526003543?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0020025526003543?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,5,8]],"date-time":"2026-05-08T19:11:14Z","timestamp":1778267474000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0020025526003543"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,8]]},"references-count":50,"alternative-id":["S0020025526003543"],"URL":"https:\/\/doi.org\/10.1016\/j.ins.2026.123423","relation":{},"ISSN":["0020-0255"],"issn-type":[{"value":"0020-0255","type":"print"}],"subject":[],"published":{"date-parts":[[2026,8]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"TumorNet: A hybrid lightweight framework for brain tumor classification and reasoning","name":"articletitle","label":"Article Title"},{"value":"Information Sciences","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.ins.2026.123423","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier Inc. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"123423"}}