Myocardial Infarction Detection Using Video Frame Key-point and Gradient Matching

Myocardial infarction is the leading cause of mortality worldwide. The most common detection methodology for this disease relies on the use of 2D echocardiography to capture left ventricular wall motions. However, these are prone to image quality issues which, in worst cases, exhibit high noise, blurriness, and incomplete extracted regions. In this paper, we tackle the problem of detecting myocardial infarction in low-quality 2D echocardiograms using the Hamad Medical Corporation and Qatar University benchmark dataset. The contributions of this paper are as follows: 1) use image enhancement techniques to improve segmentation results of the left ventricular wall with low variation; 2) apply handcrafted feature descriptors using Harris corner detector and Histogram of Oriented Gradients; and 3) use of data augmentations to improve generalizability of deep learning models on the limited number of echo frames. Our work outperformed the baseline results for the segmentation task yielding scores of 99.76%, 94.68%, 94.64%, and 99.54% for specificity, precision, F1, and accuracy, respectively. On the classification task, ResNest50 yielded the best specificity score with 96.64%.