Heart disease prediction for ECG images using CNN models

Heart disease continues to be a leading cause of global mortality, underscoring the urgent need for timely, accurate, and scalable diagnostic solutions. Traditional ECG interpretation is typically manual, time-consuming, and error-prone, creating a demand for intelligent automated systems. This paper presents a deep learning-based framework utilizing Convolutional Neural Networks (CNNs) for the classification of ECG images across multiple cardiac conditions. The proposed system incorporates advanced architectures, DenseNet169 and MobileNet, alongside a baseline CNN to enhance feature extraction and classification precision. A well-curated and preprocessed ECG dataset comprising five diagnostic categories (AHB, HMI, MI, COVID-19, and Normal) is used to train and validate the models. Experimental results demonstrate that DenseNet169 outperforms other architectures, achieving a classification accuracy of 82%, with high sensitivity in detecting both critical and subtle anomalies. Moreover, the system's ability to extend detection beyond conventional heart disease, covering COVID-19-related abnormalities and myocardial infarctions, makes it particularly relevant in current healthcare contexts. The findings highlight the potential of CNNs as non-invasive, efficient, and robust tools to support clinical decision-making and enhance early diagnosis.