Automatic Heart Disease Diagnosis Based on MRI Image Using Deep Neural Network

Abstract

In this chapter, the authors propose an adaptive bacterial foraging optimization (ABFO) algorithm for selection of features to increase the classification accuracy of heart disease diagnosis. In this approach, noises contained in the cardiac image are removed using median filter initially. Then, GLCM features are extracted from the cardiac image. Among the extracted features, optimal features are chosen using the ABFO algorithm. These selected features are then input to the classifier, which is a support vector neural network (RBNN). The classifier classifies the image into normal and abnormal. Simulation results show that the ABFO-based RBNN performs better than the conventional RBNN, ANN, KNN, and SVM in terms of accuracy.