Detection of Cardiovascular Diseases in ECG Images Using Deep Learning Methods

Abstract

Cardiovascular diseases constitute the primary killers. worldwide. The earlier they can be predicted and classified the more lives can be saved. According to the (WHO), in 2022, C-V-D claimed the lives of approximately 19.1 million people, accounting for 33% of global fatalities. (ECG) is popular for use in automated recognition of C-V-D using traditional machine learning. However, it is usually difficult to select optimal features. The Electrocardiogram ((ECG)) Signals are mainly used as diagnostic tools that routinely assess the electrical and muscular function of the heart. The proposed work comprising data collection, storage, and processing components, the system employs a convolutional neural network (CNN) model for cardiac abnormality prediction. Different image pre-processing method used to improve quality individual (ECG) images the performance indicators of the proposed C-N-N model achieves 94.87% accuracy, 68.08% recall, 84.16% precision.