{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,28]],"date-time":"2026-10-28T12:45:09Z","timestamp":1793191509159,"version":"4.5.0"},"reference-count":23,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2026,4,6]],"date-time":"2026-04-06T00:00:00Z","timestamp":1775433600000},"content-version":"vor","delay-in-days":95,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["BioMed Research International"],"published-print":{"date-parts":[[2026,1]]},"abstract":"<jats:p>\n                    Multiple sclerosis (MS) is a chronic neurological disorder commonly diagnosed through magnetic resonance imaging (MRI). Manual interpretation of MRI scans is time\u2010consuming and prone to observer variability. This study explores the use of deep learning (DL) models to automate MS lesion detection in MRI images. Four convolutional neural network (CNN) architectures\u2014AlexNet, VGG16 pretrained on ImageNet, ResNet\u201010, and DenseNet\u2010121\u2014were utilized with slight modifications to optimize their performance for medical imaging tasks. The dataset, comprising 2831 FLAIR and T2 MRI slices from 60 patients, was extensively preprocessed, including normalization, resizing, and augmentation. The models were trained using the Adam optimizer and binary cross\u2010entropy loss function and evaluated on metrics such as accuracy, precision, recall, and\n                    <jats:italic>F<\/jats:italic>\n                    1\u2010score. VGG16 demonstrated the highest performance, with an area under the curve (AUC) of 0.94, followed by DenseNet\u2010121 with an AUC of 0.91, and both AlexNet\u2010C and ResNet\u201010 with an AUC of 0.90. VGG16 also achieved the highest precision (0.81) and recall (0.93), resulting in the top\n                    <jats:italic>F<\/jats:italic>\n                    1\u2010score (0.86). ResNet\u201010, however, showed the best balance between efficiency and performance, making it highly suitable for deployment in resource\u2010constrained environments, while DenseNet\u2010121 and AlexNet\u2010C were resource\u2010inefficient given their comparable performance to ResNet\u201010. These findings underscore the potential of DL models to automate MS lesion detection, offering reliable, efficient, and scalable solutions for clinical diagnostics.\n                  <\/jats:p>","DOI":"10.1155\/bmri\/5726771","type":"journal-article","created":{"date-parts":[[2026,4,7]],"date-time":"2026-04-07T05:32:44Z","timestamp":1775539964000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Deep Learning Approaches for Multiple Sclerosis Detection in MRI Images"],"prefix":"10.1155","volume":"2026","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-6319-5396","authenticated-orcid":false,"given":"Mohamad Marwan","family":"El Sidani","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-3478-6090","authenticated-orcid":false,"given":"Rita","family":"Younes","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2747-4092","authenticated-orcid":false,"given":"Charles","family":"Yaacoub","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0614-3438","authenticated-orcid":false,"given":"Roy","family":"Abi Zeid Daou","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,4,6]]},"reference":[{"key":"e_1_2_11_1_2","volume-title":"Understanding MS Diagnosis: Exploring Symptoms, Tests, and Early Detection","year":"2024"},{"key":"e_1_2_11_2_2","volume-title":"Artificial Intelligence and Autoimmune Diseases, Studies in Computational Intelligence","author":"Mohammed Aarif K. 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Very Deep Convolutional Networks for Large-Scale Image Recognition 2014 https:\/\/arxiv.org\/abs\/1409.1556."},{"key":"e_1_2_11_20_2","doi-asserted-by":"publisher","DOI":"10.17632\/8bctsm8jz7.1"},{"key":"e_1_2_11_21_2","article-title":"Visualizing the Loss Landscape of Neural Nets","volume":"31","author":"Li H.","year":"2018","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_2_11_22_2","doi-asserted-by":"publisher","DOI":"10.21037\/jmai.2019.09.06"},{"key":"e_1_2_11_23_2","volume-title":"Proceedings of the 2023 Seventh International Conference on Advances in Biomedical Engineering","author":"Younes R.","year":"2023"}],"container-title":["BioMed Research International"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1155\/bmri\/5726771","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/full-xml\/10.1155\/bmri\/5726771","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1155\/bmri\/5726771","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,7]],"date-time":"2026-04-07T05:32:49Z","timestamp":1775539969000},"score":1,"resource":{"primary":{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/10.1155\/bmri\/5726771"}},"subtitle":[],"editor":[{"given":"Baisakhi","family":"Banerjee","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"editor"}]}],"short-title":[],"issued":{"date-parts":[[2026,1]]},"references-count":23,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2026,1]]}},"alternative-id":["10.1155\/bmri\/5726771"],"URL":"https:\/\/doi.org\/10.1155\/bmri\/5726771","archive":["Portico"],"relation":{},"ISSN":["2314-6133","2314-6141"],"issn-type":[{"value":"2314-6133","type":"print"},{"value":"2314-6141","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,1]]},"assertion":[{"value":"2025-08-21","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2026-02-09","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2026-04-06","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}],"article-number":"5726771"}}