Journal Article

A hybrid segmentation and classification CAD framework for automated myocardial infarction prediction from MRI images

Lee, Soojeong, Al-Tam, Riyadh M., Al-antari, Mugahed A., Gu, Yeong Hyeon, Al-Hejri, Aymen M., Al-Huda, Zaid, Yıldırım, Özal

2025

Overview

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.

• Publisher: Nature Publishing Group UK,Nature Portfolio
• Subject: 639/166/985 / 692/700/1421 / Algorithms / Classification / Computer-Aided diagnosis / Deep Learning / Diagnosis, Computer-Assisted - methods / Ensemble fusion learning / Heart diseases / Humanities and Social Sciences / Humans / Image Interpretation, Computer-Assisted - methods / Image Processing, Computer-Assisted - methods / Magnetic Resonance Imaging - methods / multidisciplinary / Myocardial infarction / Myocardial Infarction - diagnosis / Myocardial Infarction - diagnostic imaging / Science / Science (multidisciplinary) / Segmentation