Deep Transfer-Learning Framework for Automated Cardiovascular Disease Prediction from ECG Images

We present a comprehensive deep transfer learning framework for the automated prediction of cardiovascular disease (CVD) from electrocardiogram (ECG) images, utilizing multiple state-of-the-art pretrained convolutional neural networks. Raw ECG waveforms are transformed into standardized image inputs via uniform scaling, noise reduction, and noise-resistant plotting. To improve the generalization of the model, comprehensive augmentation methods—including time-warping, random cropping, luminance modification, Gaussian noise addition, rotation, and elastic distortions—are employed to mimic the variability observed in real-world ECG data. The proposed framework incorporates multiple pretrained architectures, including VGG19, ResNet-101, DenseNet121, InceptionV1, Xception, MobileNetV3, and NASNet, with the initial layers preserving generic visual features and the deeper layers being fine-tuned to capture ECG-specific morphological and temporal characteristics. Transfer-learning optimization is enhanced through selective layer freezing, discriminative learning rates, adaptive pooling, and feature-space alignment. Class imbalance is addressed through the application of focal loss, weighted oversampling, and the generation of balanced batches. Model interpretability is achieved via saliency maps, Grad-CAM, and layer-wise relevance propagation to emphasize diagnostically significant regions of the waveform. Experimental assessment on publicly accessible ECG datasets utilizing stratified cross-validation exhibits high sensitivity, specificity, and robustness across all backbone models, while preserving computational efficiency appropriate for near-real-time deployment. This integrated transfer-learning methodology diminishes reliance on manually engineered features and offers an accurate, scalable, and interpretable solution for ECG-based cardiovascular disease screening.