Transfer Learning Strategies for Cardiovascular Disease Detection in ECG Imagery

Background: Coronary artery disease (CAD) remains one of the leading causes of death globally. Traditional manual scoring methods using non-contrast computed tomography (NCCT) are time-consuming, subjective, and require expertise. To overcome these limitations, this research introduces an AI-driven model to predict and classify more efficiently and accurately. Convolutional Neural Networks (CNNs) are a crucial deep learning tool for detecting cardiovascular diseases (CVDs) from ECG images due to their ability to automatically extract complex patterns and hierarchical features. DenseNet201 is a deep learning model effectively used for cardiovascular disease (CVD) detection from ECG imagery, demonstrating high accuracy in classifying cardiac conditions, particularly for multi-class scenarios. InceptionV3 is a deep learning model widely used for cardiovascular disease (CVD) detection from electrocardiogram (ECG) imagery by leveraging its fine-tuned architecture to classify cardiac conditions. Objectives: To develop a deep learning-based model for automatic classification and prediction of coronary artery calcium scores. To enhance accuracy using an improved BiGRU model incorporating, to reduce the error and bias in current automatic scoring systems and improve clinical decision-making. Design: The study designs a novel architecture named HeProbAtt BiGRU Net. The model performs both classification (healthy vs non-healthy) and regression on NCCT image data. Methods: Data collection, 14 127 NCCT slices-dataset from Tabriz University of Medical Sciences, Preprocessing, Model Development, Performance Evaluation Metrics: Accuracy, precision, recall, F1-score, ROC-AUC, MAE, RMSE. Results: The proposed model outperformed all compared models with: Classification: Accuracy = 99%, F1-score = 99%, ROC-AUC = .99, Regression: MAE = .065, RMSE = .145. The inclusion of attention and probabilistic weights enhanced learning efficiency and decision precision. Visualization tools (eg, loss curves, confusion matrix, ROC) showed stable and high-performing learning behavior. Conclusion: The HeProbAtt BiGRU Net provides a highly accurate, automated, and efficient method for coronary artery calcium scoring. Its hybrid framework allows real-time classification and regression, aiding clinicians in early CAD diagnosis. Future work could include validation on larger, multi-center datasets, and incorporation of clinical explain-ability features.