A Review of Machine Learning and Deep Learning Techniques for Identifying Cardiovascular Disease in ECG Images

Globally Cardiovascular diseases (CVDs) rank among the top causes of death.Electrocardiogram (ECG) readings provide vital information necessary for diagnosing heart ailments.Techniques in Machine Learning (ML) and Deep Learning (DL) have transformed the automatic identification and categorization of CVDs from ECG images.This paper offers a detailed overview of ML and DL approaches utilized in CVD detection, highlighting various algorithms, datasets, and performance metrics.It can be used in future research for challenges and potential in this area.The study underscores the efficacy of convolutional neural networks (CNNs), recurrent neural networks (RNNs), and hybrid models in improving diagnostic accuracy.