Cardiovascular Disease Detection in ECG images using CNN - MobilNet Model

Abstract

As cardiovascular diseases (CVDs) persist as the world's leading cause of death, a timely and precise diagnosis is crucial. Using electrocardiogram (ECG) measurements, which provide crucial information in this respect, automating the recognition of cardiac disorders has shown enormous promise thanks to neural network methods. The use of a Convolutional Neural Network, also based on the MobileNet model, for identifying the presence of cardiovascular abnormalities from ECG pictures, is looked at in this work. The MobilNet model, well-known for its efficiency and portability, is used to extract high-dimensional features. This makes it possible to classify ECG patterns associated with various heart conditions accurately. When contrasted with conventional techniques, findings from experiments suggest that the recommended approach has been effective in achieving excellent accuracy and adaptability. The proposed approach could assist healthcare professionals in quickly and accurately diagnosing patients, ultimately leading to better patient outcomes.