A CNN and ResNet 50 Approach to Detect Cardiac Diseases using ECG Images

Abstract

The biggest cause of mortality globally is cardiac illness. If the initial diagnosis was more accurate, cardiac problems may be avoided. ECG testing is typically used as a diagnostic technique to screen for heart disorders. The electric cardiac signal is recorded by an ECG to look for various heart conditions. Utilizing a variety of datasets, a number of algorithms and methodologies have been developed to detect different heart illnesses. However, this study proposes to examine whether convolutional neural network (CNN) model and ResNet 50 can be used to identify cardiac illnesses such arrhythmia or abnormal heartbeat (AHB), myocardial infarction (MI), and previous history of MI (PMI) from electrocardiogram (ECG) trace images. A 1937 ECG image dataset from Kaggle is examined... The ECG pictures in the dataset are separated into four categories: normal, MI, AHB, and PMI. The suggested approach examined categorization of normal and various heart disorders with 99.12% accuracy (MI, AHB, and PMI)