PSq-Net: parallel SqueezeNet for myocardial infarction detection using echocardiography

Nowadays, myocardial infarction has become a major cause of sudden and unexpected deaths globally, which is caused by the death of heart muscle cells due to blocked coronary arteries. In this study, parallel SqueezeNet (PSq-Net) is introduced for the detection of myocardial infarction (MI) from echocardiography videos. Initially, the input video is obtained and then, pre-processing the video frames using median and Gaussian filters to eliminate unwanted noise. Next, the left ventricular (LV) wall and endocardial boundary regions are segmented using stack attention U-Net (SAUN). From the segmented endocardial boundary region, the features are extracted using local binary patterns (LBP), local optimal oriented patterns (LOOP), and statistical measures. Finally, myocardial infarction is detected from these features using PSq-Net, and it is the hybridisation of parallel convolutional neural network (PCNN) and SqueezeNet. The PSq-Net demonstrates a high sensitivity of 94.18%, accuracy of 93.09%, and specificity of 93.64%.