Deep Convolutional Network Modeling for ECG Image Analysis in Cardiovascular Disease Detection

Cardiovascular diseases (CVDs) remain the foremost cause of global mortality, accounting for nearly one-third of deaths worldwide. Early detection of cardiac abnormalities is essential to reduce mortality and ensure timely treatment. Electrocardiograms (ECGs) are among the most widely used diagnostic tools for monitoring cardiac activity. However, manual interpretation of ECGs is error-prone and highly dependent on medical expertise. This paper presents a Convolutional Neural Network (CNN)-based framework for automated ECG image classification. The dataset, consisting of 928 ECG images across four categories— Normal, Abnormal, History of Myocardial Infarction (HMI), and Myocardial Infarction (MI) was preprocessed through grayscale conversion, noise reduction, cropping, resizing, and normalization. A custom CNN architecture was trained on this dataset, achieving a classification accuracy of 97.92% on test data, with strong precision, recall, and F1-scores across all categories. The system was deployed using a Flask-based web application that provides real-time predictions and visualizations. The proposed solution demonstrates the applicability of deep learning in medical diagnostics, offering a reliable and scalable approach for CVD detection.