Enhanced SVM–KPCA Method for Brain MR Image Classification. (26th April 2019)

• Record Type: Journal Article
• Authors: Neffati, Syrine; Ben Abdellafou, Khaoula; Taouali, Okba; Bouzrara, Kais
• Is Part Of: Computer journal. Volume 63:Number 3(2020)
• Page Start: 383
• Page End: 394
• Publication Date: 2019-04-26
• Subjects: KPCA -- deep multiple kernel learning -- machine learning -- SVM -- medical imaging -- feature extraction -- pre-diagnosis -- pattern recognition; Computers -- Periodicals; 005.1
• DOI: 10.1093/comjnl/bxz035
• Languages: English
• ISSNs: 0010-4620

Abstract

Abstract: 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–KPCA 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–KPCA algorithm. Three databases are used to validate the suggested SVM–KPCA method. Three conclusions are obtained from this work. First, KPCA is highly efficient in increasing the classifier's performance compared with similar algorithms working on the proposed database. Second, the SVM–KPCA 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.