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In this study, a hybrid framework combining deep learning-based feature extraction, metaheuristic feature selection, and machine learning (ML) classifiers is proposed for accurate MS classification. All MRI images were preprocessed using Contrast Limited Adaptive Histogram Equalization (CLAHE), resizing, and normalization to enhance contrast and standardize the input dimensions. Deep features were extracted using the pretrained VGG16 convolutional neural network (CNN), in which the fully connected layers were removed, and the convolutional base was used to obtain high-dimensional features per image. To reduce dimensionality and improve classification performance, the Whale Optimization Algorithm (WOA) was employed to select the most discriminative subset of features using a Support Vector Machine (SVM)-based fitness function. Multiple classifiers were then trained and evaluated using the optimized feature set. Among them, the Artificial Neural Network integrated with WOA (ANN+WOA) achieved the highest classification accuracy of 98%, demonstrating the potential of the proposed model for reliable, efficient, and automated MS diagnosis.<\/jats:p>","DOI":"10.3389\/fnhum.2025.1685580","type":"journal-article","created":{"date-parts":[[2026,1,12]],"date-time":"2026-01-12T08:16:44Z","timestamp":1768205804000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Hybrid deep learning and feature optimization approach for early detection of multiple sclerosis"],"prefix":"10.3389","volume":"19","author":[{"given":"Nandini","family":"Anam","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sharief Basha","family":"S.","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chiranji Lal","family":"Chowdhary","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,1,12]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"1139","DOI":"10.1007\/s11277-021-09054-2","article-title":"Leaf image-based plant disease identification using color and texture features.","volume":"121","author":"Ahmad","year":"2021","journal-title":"Wireless Personal Commun."},{"key":"B2","doi-asserted-by":"publisher","first-page":"2911","DOI":"10.3390\/s22082911","article-title":"Brain MRI analysis for Alzheimer\u2019s disease diagnosis using CNN-based feature extraction and machine learning.","volume":"22","author":"AlSaeed","year":"2022","journal-title":"Sensors"},{"key":"B3","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.neuroimage.2019.03.068","article-title":"Multi-branch convolutional neural network for multiple sclerosis lesion segmentation.","volume":"196","author":"Aslani","year":"2019","journal-title":"NeuroImage"},{"key":"B4","doi-asserted-by":"publisher","first-page":"321","DOI":"10.1038\/s41586-023-06618-z","article-title":"Elevated genetic risk for multiple sclerosis emerged in steppe pastoralist populations.","volume":"625","author":"Barrie","year":"2024","journal-title":"Nature"},{"key":"B5","volume-title":"The essential physics of medical imaging.","author":"Bushberg","year":"2011"},{"key":"B6","doi-asserted-by":"publisher","first-page":"77","DOI":"10.17712\/nsj.2024.2.20230103","article-title":"Diagnostic effectiveness of deep learning-based MRI in predicting multiple sclerosis: A meta-analysis.","volume":"29","author":"Daqqaq","year":"2024","journal-title":"Neurosci. 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