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<p>Detection of coronary artery stenosis and risk stratification of troponin plays 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 clinical relevant. The proposed model was found to have a precision of 0.991, a recall value of 0.960, 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 in addition 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.</p>

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Modified YOLOv8x model for coronary stenosis detection and troponin risk stratification

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年1月20日

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  • Roaa Albasrawi,
  • Muhammad Ilyas,
  • Qutaibah Althebyan

摘要

Detection of coronary artery stenosis and risk stratification of troponin plays 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 clinical relevant. The proposed model was found to have a precision of 0.991, a recall value of 0.960, 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 in addition 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.