{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,19]],"date-time":"2026-02-19T04:39:24Z","timestamp":1771475964348,"version":"3.50.1"},"reference-count":45,"publisher":"Oxford University Press (OUP)","issue":"3","license":[{"start":{"date-parts":[[2019,4,26]],"date-time":"2019-04-26T00:00:00Z","timestamp":1556236800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020,3,18]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Automated classification of magnetic resonance brain images (MRIs) is a hot topic in the field of medical and biomedical imaging. Various methods have been suggested recently to improve this technology. In this paper, to reduce the complexity involved in the medical images and to ameliorate the classification of MRIs, a novel 3D magnetic resonance (MR) brain image classifier using kernel principal component analysis (KPCA) and support vector machines (SVMs) is proposed. Experiments are carried out using A deep multiple kernel SVM (DMK-SVM) and a regular SVM. An algorithm entitled SVM\u2013KPCA is put forward. Its main task is to classify a brain MRI as a normal brain image or as a pathological brain image. This algorithm, firstly, adopts the discrete wavelet transform technique to extract features from images. Secondly, KPCA is applied to decrease the dimensionality of features. SVM is then applied to the reduced data. A K-fold cross-validation strategy is used to avoid overfitting and to ameliorate the generalization of the SVM\u2013KPCA algorithm. Three databases are used to validate the suggested SVM\u2013KPCA method. Three conclusions are obtained from this work. First, KPCA is highly efficient in increasing the classifier\u2019s performance compared with similar algorithms working on the proposed database. Second, the SVM\u2013KPCA algorithm performs well in differentiating between two classes of medical images. Third, the approach is robust and might be utilized for other MRIs. This proposes a significant role for computer aided diagnosis analysis systems used for clinical practice.<\/jats:p>","DOI":"10.1093\/comjnl\/bxz035","type":"journal-article","created":{"date-parts":[[2019,4,1]],"date-time":"2019-04-01T15:10:34Z","timestamp":1554131434000},"page":"383-394","source":"Crossref","is-referenced-by-count":22,"title":["Enhanced SVM\u2013KPCA Method for Brain MR Image Classification"],"prefix":"10.1093","volume":"63","author":[{"given":"Syrine","family":"Neffati","sequence":"first","affiliation":[{"name":"Electrical Department, National Engineering School of Monastir, University of Monastir, 5019 Monastir, Tunisia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Khaoula","family":"Ben Abdellafou","sequence":"first","affiliation":[{"name":"Department of Computer Science, Faculty of Computers and Information Technology, University of Tabuk, 71491 Tabuk, Saudi Arabia"},{"name":"University of Sousse, ISITCom, MARS Research Laboratory, LR17ES05, 4011, Hammam Sousse, Tunisia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Okba","family":"Taouali","sequence":"first","affiliation":[{"name":"Department of Computer Engineering, Faculty of Computers and Information Technology, University of Tabuk, 71491 Tabuk, Saudi Arabia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kais","family":"Bouzrara","sequence":"first","affiliation":[{"name":"Electrical Department, National Engineering School of Monastir, University of Monastir, 5019 Monastir, Tunisia"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2019,4,26]]},"reference":[{"key":"2020042105454014200_bxz035C1","volume-title":"Handbook of MRI Technique","author":"Westbrook","year":"2014"},{"key":"2020042105454014200_bxz035C2","doi-asserted-by":"crossref","first-page":"2375","DOI":"10.1007\/s13369-013-0559-4","article-title":"Enhanced possibilistic fuzzy C-means algorithm for normal and pathological brain tissue segmentation on magnetic resonance brain image","volume":"38","author":"Rajendran","year":"2013","journal-title":"Arabian J. 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