Heart Disease Evaluation Through Echocardiography Using CNN, ResetNet50, VGG16, and Image Processing - Suvarna Potdukhe; Akanksha Karale; Aniket Deshmukh; Shivani Ghare; Mayuri Kadam (International Journal of Information Security Engineering, 2024)
Heart conditions stand out as primary contributors to untimely mortality among adults aged 30 and above, notably among those grappling with elevated cholesterol levels and diabetes. Detecting such ailments often necessitates the use of an echocardiogram, providing an intricate portrayal of the heart. However, precise analysis hinges on both the proper functioning of the echocardiogram apparatus and the proficiency of a skilled radiologist, a condition not always met. Manual scrutiny of echocardiograms to identify heart conditions proves to be an arduous endeavor prone to human fallibility, including misinterpretations, thus advocating for automation through image processing techniques like convolutional neural networks (CNN). This risk of misinterpretation escalates in remote locales where radiologist training might be deficient compared to urban centers, compounded by the potential presence of defective equipment. To tackle these obstacles, we introduce a system aimed at automating heart disease prediction based on echocardiograms. By minimizing human error, we aim to uphold consistent healthcare standards across urban and rural medical facilities. For the preliminary phase of our endeavor, we evaluated CNN, ResNet50, and VGG16 algorithms, ultimately selecting CNN due to superior accuracy. Specifically, our system concentrates on identifying angina pectoris, cardiovascular disease, coronary artery disease, and left ventricular hypertrophy (LVH). In initial testing, our CNN model achieved an accuracy rate of 95.27%.
Keywords: CNN (convolutional neural networks); ResetNet50; VGG16; echocardiography; image processing