Classification of Cardiac Abnormalities using Deep Learning and Machine Learning Methods

Cardiovascular disease detection through ECG image analysis presents a critical challenge in modern healthcare diagnostics. This research introduces an innovative approach combining SqueezeNet architecture with advanced data augmentation techniques for automated cardiac abnormality classification. Our study analyzes a comprehensive dataset of ECG images representing four distinct cardiac conditions: normal rhythm, abnormal heartbeat, myocardial infarction, and historical myocardial infarction cases. We hyperparameter tuning; second, comparing its performance with AlexNet and a lightweight CNN; and third, utilizing these architectures charcteristic extractors for traditional machine learning classifiers. The optimized SqueezeNet model achieves remarkable results with 99.85% accuracy, 99.7% precision, 99.8% recall, and 99.8% F1-score, significantly outperforming existing approaches. Notable improvements include enhanced feature extraction capabilities, demonstrated by achieving 99.85% accuracy when combined with the SVM classifier. The model's efficiency is particularly evident in its reduced computational requirements, made it suitable for deployment on resource restraints devices. Impact Statement—This research advances cardiovascular disease detection through an optimized deep learning approach that balances accuracy with computational efficiency. The enhanced SqueezeNet architecture demonstrates superior performance in ECG image classification while maintaining minimal computational requirements, making it ideal for real-world clinical applications. Our methodology's success in achieving 99.8% accuracy through transfer learning and 99.79% accuracy using feature extraction with traditional classifiers represents a significant advancement in automated cardiac diagnosis. This approach's practicality and efficiency make it particularly valuable for integration into existing healthcare systems and IoT-based medical devices, potentially improving access to rapid cardiac diagnosis in various healthcare settings. The study's findings provide a foundation for future research in automated medical image analysis and practical implementation of AI in healthcare diagnostics.