Modified YOLOv8x model for coronary stenosis detection and troponin risk stratification

Article

Jan 20, 2026

DOI:

Roaa Albasrawi Muhammad Ilyas Qutaibah Althebyan

Abstract

Detection of coronary artery stenosis and risk stratification of troponin play a pivotal role in offering early diagnosis and treatment of cardiovascular diseases. In this paper, an improved deep learning framework that allows using both spatial and frequency-based attention mechanisms will be proposed using a modified YOLOv8x framework. Upon benchmarking YOLOv8, YOLOv9, and YOLOv10 models, YOLOv8x was chosen due to its excellent baseline, and the enhancement was done to make it more clinically relevant. The proposed model was found to have a precision of 0.991, a recall value of 0.960, an F1-score of 0.980, and a mAP of 0.976. These findings show significant possibilities of real-world applications. The effectiveness of the improvements is additionally validated by large-scale ablation studies, and the results overcome the problem of detecting fine lesions and disparate clinical information. The work has added value in the form of a reliable end-to-end diagnostic cardiovascular imaging and biomarker-based risk analysis.