International Conference on Information and Knowledge Technology

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Attention-Enhanced Ensemble Learning for Automated Stenosis Detection in X-ray Coronary Angiography Videos

Authors :

Marzieh Sadat Hosseini¹ Ahmad R. Naghsh-Nilchi² Mehran Safayani³ Masoumeh Sadeghi⁴

1- دانشگاه اصفهان 2- دانشگاه اصفهان 3- دانشگاه صنعتی اصفهان 4- دانشگاه علوم پزشکی اصفهان

Keywords :

Coronary artery disease،stenosis detection،stacked ensemble learning،X-ray coronary angiography،key frame selection،attention

Abstract :

Coronary artery disease is a major global health concern that requires timely and accurate diagnosis to prevent life-threatening outcomes. While X-ray coronary angiography (XCA) is the clinical gold standard for detecting stenosis, its interpretation remains subjective and variable across observers. This paper presents a deep learning-based framework for automated stenosis detection in XCA videos, focusing on the right coronary artery. From each video, five key frames are extracted using uniform sampling and refined through a segmentation-guided selection method. We employ a stacked ensemble model that combines VGG16 and VGG19 architectures, each enhanced with a Convolutional Block Attention Module to emphasize salient features. Frame-level predictions are aggregated via majority voting to achieve video-level classification. Experimental results on 413 patient cases using five-fold stratified cross-validation show that our model outperforms individual CNN baselines, achieving F1-scores of 77.19% and 77.61% at the frame and video levels, respectively. The results highlight the effectiveness of attention-guided ensemble learning for robust stenosis detection.