Heart Disease Prediction using ECG Image Analysis with Grad-CAM Explainability Visualization

The following are the major contributions of this research:
1. An end-to-end ECG preprocessing and region-of-interest (ROI) based segmentation system that helps to extract diagnostically useful waveform regions before model training.
2. Comparative performance analysis of different DL architectures (VGG16, ResNet50, DenseNet121, MobileNetV2, EfficientNetB0, EfficientNetB0 with CBAM Attention mechanism, and hybrid Fusion) in the same experimental setting.
3. Use of Grad-CAM for interpretation of the waveforms responsible for each prediction and thereby making decision-making clinically reliable.
4. Real-time prediction system using a GUI interface that enables clinicians to upload ECG images and get interpretable predictions.