Enhancing Heart Disease Diagnosis Through Advanced Deep CNNs for Multi-Class ECG Image Classification

Heart disease remains a leading cause of mortality worldwide, necessitating rapid and accurate diagnostic systems to enhance patient outcomes. Traditional electrocardiogram (ECG) analysis methods are prone to errors, particularly when dealing with complex patterns or large datasets. This paper proposes an advanced deep learning approach using Convolutional Neural Networks (CNNs) for multi-class ECG image classification, aimed at improving diagnostic accuracy and efficiency. The methodology incorporates techniques such as transfer learning, data augmentation, and Generative Adversarial Networks (GANs) to enhance model robustness and performance. A user-friendly interface is developed for real-time diagnostic application, promoting clinical integration. Comparative analysis reveals that the proposed model achieves an overall accuracy of 94.7%, significantly outperforming traditional methods such as Support Vector Machine (SVM), Random Forest, K-Nearest Neighbors (KNN), and Decision Tree. These findings demonstrate the potential of deep learning-based systems to revolutionize cardiac diagnosis, providing scalable, accurate, and efficient diagnostic solutions applicable to diverse clinical environments.