TY  - EJOU
AU  - Atteia, Ghada 
AU  - Altamimi, Abdulaziz 
AU  - Abuzinadah, Nihal 
AU  - Alnowaiser, Khaled 
AU  - Umer, Muhammad 
AU  - Nam, Yunyoung 
AU  - Cho, Yongwon 

TI  - Explainable Segmentation-Guided Mamba-Transformer Framework for Automated Cardiovascular Disease Detection
T2  - Computer Modeling in Engineering \& Sciences

PY  - 2026
VL  - 147
IS  - 1
SN  - 1526-1506

AB  - Cardiovascular diseases (CVD) remain the leading cause of global mortality, making early and accurate diagnosis essential for improving patient outcomes. However, most existing deep learning approaches address cardiac image segmentation or disease classification independently, limiting their effectiveness in complex clinical decision-making scenarios. In this study, we propose an explainable spatio-temporal deep learning framework that integrates segmentation-guided representation learning with efficient temporal modeling for automated CVD detection. The proposed architecture incorporates the Segment Anything Model for Medical Imaging in 2D (SAM-Med2D) to achieve accurate cardiac structure segmentation, followed by Mamba-based temporal feature extraction and Transformer-driven spatial representation learning to capture both dynamic motion patterns and anatomical dependencies in cardiac imaging sequences. To enhance transparency and clinical trust, Gradient-weighted Class Activation Mapping (Grad-CAM) and SHapley Additive exPlanations (SHAP) are employed to provide interpretable diagnostic insights. The framework is evaluated on three benchmark cardiovascular datasets, including EchoNet-Dynamic, CAMUS echocardiography, and UK Biobank cine cardiac magnetic resonance imaging (CMR). Experimental results demonstrate strong performance, achieving a Dice score of 91.20% for segmentation, an AUC of 95.50%, classification accuracy of 92.10%, and an MCC of 0.84, consistently outperforming multiple baseline methods. The proposed framework consistently outperforms baseline and existing methods, achieving approximately 3%–6% improvement in segmentation performance and 3%–4% improvement in classification accuracy across key evaluation metrics. The proposed approach offers a robust and explainable solution for automated cardiovascular disease detection, with significant potential to support reliable clinical deployment and improve diagnostic workflows in medical imaging practice.
KW  - Medical imaging; explainable artificial intelligence; transformer; segmentation; cardiovascular disease detection

DO  - 10.32604/cmes.2026.078510