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Abstract

Cardiovascular diseases (CVDs) remain a leading cause of mortality worldwide, necessitating early and precise detection to improve patient outcomes. Traditional diagnostic approaches rely on single-modal imaging, which often lacks the depth required for accurate prognostics. The integration of multimodal medical imaging enhances diagnostic accuracy by leveraging complementary information from multiple imaging techniques, such as MRI, CT, and echocardiography. However, effectively processing and analyzing this high-dimensional data remains a significant challenge. To address this, a Dense Residual Network (DenseResNet)-powered deep learning model is proposed for early CVD detection. The method employs multimodal feature fusion to extract relevant spatial and temporal features, enabling comprehensive disease identification. The DenseResNet architecture, with its densely connected residual blocks, enhances gradient flow and prevents vanishing gradients, thereby improving model stability and convergence. The proposed approach undergoes rigorous training and validation using a dataset comprising multimodal cardiac images. Experimental results demonstrate superior performance compared to conventional deep learning models, achieving an accuracy of 98.4%, sensitivity of 97.8%, and specificity of 98.1% in CVD classification.