A hybrid segmentation and classification CAD framework for automated myocardial infarction prediction from MRI images - Al-Antari, Mugahed A.; Al-Tam, Riyadh M.; Al-Hejri, Aymen M.; Al-Huda, Zaid; Lee, Soojeong; Yildirim, Oezal; Gu, Yeong Hyeon (Nature Portfolio, 2025)
Early diagnosis of myocardial infarction (MI) is critical for preserving cardiac function and improving patient outcomes through timely intervention. This study proposes an annovaitive computer-aided diagnosis (CAD) system for the simultaneous segmentation and classification of MI using MRI images. The system is evaluated under two primary approaches: a serial approach, where segmentation is first applied to extract image patches for subsequent classification, and a parallel approach, where segmentation and classification are performed concurrently using full MRI images. The multi-class segmentation model identifies four key heart regions: left ventricular cavity (LV), normal myocardium (Myo), myocardial infarction (MI), and persistent microvascular obstruction (MVO). The classification stage employs three AI-based strategies: a single deep learning model, feature-based fusion of multiple AI models, and a hybrid ensemble model incorporating the Vision Transformer (ViT). Both segmentation and classification models are trained and validated on the EMIDEC MRI dataset using five-fold cross-validation. The adopted ResU-Net achieves high F1-scores for segmentation: 91.12% (LV), 88.39% (Myo), 80.08% (MI), and 68.01% (MVO). For classification, the hybrid CNN-ViT model in the parallel approach demonstrates superior performance, achieving 98.15% accuracy and a 98.63% F1-score. These findings highlight the potential of the proposed CAD system for real-world clinical applications, offering a robust tool to assist healthcare professionals in accurate MI diagnosis, improved treatment planning, and enhanced patient care.
Keywords: Myocardial infarction; Heart diseases; Ensemble fusion learning; Computer-Aided diagnosis; Segmentation; Classification; Visual explainable saliency maps