A Hybrid Deep Learning Heart Disease Prediction Framework Utilizing Multi-Modal Medical Imaging and Novel Feature Fusion Techniques

Received: 6 July 2025 | Revised: 16 August 2025 and 23 August 2025 | Accepted: 2 September 2025 | Online: 15 October 2025

Corresponding author: P. Archana

Abstract

Heart diseases require advanced diagnostic techniques for early and accurate detection. This paper combines multi-modal data sources, such as CT images, MRI scans, and ECG signals to provide a hybrid deep learning architecture for accurate cardiac disease identification. The system uses specific feature extraction methods, such as 3D-UNet for 3D MRI and CT images and Temporal Convolutional Graph Neural Networks (TC-GNN) for ECG, and then uses genetic algorithms to optimize the features. Autoencoders, which are 1D for ECG and 3D for MRI and CT, are employed for non-linear dimensionality reduction in order to handle the high dimensionality of fused information. A Convolutional Neural Network (CNN) processes the fused compact features for the final classification. The proposed model achieved a 97.1% accuracy, outperforming known models. Accuracy, recall, F1-score, and ROC-AUC scores support its generalizability and robustness. This multi-modal and feature-aware approach significantly increases classification accuracy, reduces false positives and false negatives, and provides a scalable clinical decision support solution for cardiovascular diagnostics.