Deep Learning Framework to Predict and Diagnose the Cardiac Diseases by Image Segmentation

Abstract

Technologies have been vastly developed in all sectors of the society. Especially in medical field, its growth has been at rapid phase. The newly invented medical equipment has currently been playing massive role in saving the lives of many patients if given the proper and timely treatment to them. Among many modern medical equipment, MRI scanning deserves special mention. Using this technology, we can have the detailed images of organs inside the body as well as categorize and identify the stage of disease. Moreover, with the help of MRI, myocardial disease can be categorized and assessed with several conditions. In particular, we can save patient from a critical situation. However, it is difficult to have an accurate prediction of the cardiac disease. Furthermore, the current medical procedures require more time and medical care to accurately diagnose cardiovascular diseases. Under these circumstances, deep learning method can be useful to have a segment clear and accurate cine image in very less time. A deep learning (DL) technique has been proposed to assist the atomization of the cardiac segmentation in cardiac MRI. We have adopted three types of strategies, according to which we firstly optimize the Jaccard distance to accept the adjective function and then implement the residual learning techniques to integrate it into the code. Finally, a fully convolutional neural network (FCNN) was trained to introduce a batch normalization (BN) layer. However, our standard results show for myocardial segmentation that time taken for volume of 128 × 128 × 13 pixels is less than 23 s which is found when the process is done by using 3.1 GHz Intel Core i9 to be volume.