Enhanced AlexNet for Detecting the Myocardial Infarction: An Efficient Approach

Abstract

Heart muscle damage is a result of myocardial infarction (MI), which is caused by inadequate blood supply. Around the world, MI is the leading cause of death for middle-aged and older people. To reduce the risk of MI, early detection is important. This detection is obtained by using a deep learning algorithm. In the literature, few methods are reviewed which does not provide optimal results for detection. Hence, in this paper, the Enhanced AlexNet is developed for an effective diagnosis (ED) to identify MI signals (EAlexNet). To train AlexNet and obtain the best results, a hybrid spider monkey optimization (SMO) and salp swarm optimization (SSO) algorithm is used. Four phases are taken into consideration in the paper to find the MI signals. The input dataset is used to construct the echo frames, and the formed frames are then trained using the EAlexNet. Then, using an adaptive algorithm called a support vector machine (SVM) with kernel function, the process of feature extraction is carried out. Finally, the proposed approach is used to complete the MI classification process. The normal (non-MI) and abnormal (MI) cases are identified from the proposed model. The HMC-QU dataset is taken into account for analysis purposes, and the effectiveness of the suggested strategy is assessed. The suggested approach is contrasted with the current approaches, including ResNet, MobileNet, and VGGNet, respectively. The suggested method is put into practise using the MATLAB platform, and the accuracy, sensitivity, precision, and specificity performance analysis is examined.