Ensemble Convolutional Neural Networks With Support Vector Machine for Epilepsy Classification Based on Multi-Sequence of Magnetic Resonance Images

Abstract

Classification of brain abnormalities as a pathological cue of epilepsy based on magnetic resonance (MR) images is essential for diagnosis. There are some types of brain structural abnormalities as a pathological cue of epilepsy. To identify it, a neurologist can involve some sequence of MR images at a time. Existing algorithms for abnormalities classification usually involve only one or two sequences of MR images. In this paper, we proposed ensemble convolutional neural networks with a support vector machine (SVM) scheme to classify brain abnormalities (epilepsy) vs. non-epilepsy based on the axial multi-sequence of MR images. The convolutional neural network (CNN) models on the proposed method are base-learner models with different architectures and have low parameters. The performance improvement on the proposed method is made by combining the output of the base-learner models and the combination of predictions from these models. The combination of predictions uses majority voting, weighted majority voting, and weighted average. Henceforth, the combined output becomes input in the meta-learning process with SVM for the final classification. The dataset for evaluation is the axial multi-sequences of MR images that include abnormal brain structures causing epilepsy and non-epilepsy with various subjects' histories. The experimental results show the proposed method can obtain an accuracy average and F1-score of 86.37% and 90.75%, respectively, and an improvement of accuracy of 6.7%-18.19% against the CNN models on the base-learner and 2.54%-2.65% against the combination of predictions. With these results, the proposed architecture also provides better performance compared to the two existing CNN architectures.