![]() This algorithm, firstly, adopts the discrete wavelet transform technique to extract features from images. Its main task is to classify a brain MRI as a normal brain image or as a pathological brain image. An algorithm entitled SVM–KPCA is put forward. Experiments are carried out using A deep multiple kernel SVM (DMK-SVM) and a regular SVM. 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. Various methods have been suggested recently to improve this technology. Automated classification of magnetic resonance brain images (MRIs) is a hot topic in the field of medical and biomedical imaging.
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