Key result
Improved YOLOV5 model boosts carotid ultrasound plaque recognition accuracy by ~3.5% versus the original.
Why the study?
Traditional ultrasound testing for carotid plaque recognition has strong subjectivity, poor reproducibility, and risk of missed detections.
Does an improved YOLOV5 model improve the accuracy of carotid plaque recognition in ultrasound images compared to the original YOLOV5 model?
Does an improved YOLOV5 model improve the accuracy of carotid plaque recognition in ultrasound images compared to the original YOLOV5 model?
An improved YOLOV5 deep learning model enhances the accuracy of carotid plaque recognition in ultrasound images, offering potential improvements for automated stroke risk assessment.
Answers are grounded in this paper and cite their sources.
May facilitate AI-driven carotid plaque recognition; extends YOLOV5 applications but leaves open clinical outcome impact.
Cardiovascular disease is a common disease that poses a serious threat to humanity, with a high incidence, disability, and mortality rate that is daunting. Carotid Atherosclerosis (CAS) is closely related to cardiovascular disease, so it is very important to accurately identify carotid plaque in ultrasound images for the diagnosis of stroke risk patients. At present, traditional ultrasound testing has strong subjectivity, poor reproducibility, and there are cases of missed detections. To address these issues, this article proposes an improved carotid artery ultrasound plaque recognition and localization algorithm. Firstly, preprocess the ultrasound image to reduce the impact of noise and shadows; Secondly, by improving the YOLOV5 model, setting custom anchor frames, and adding attention mechanisms, the final accuracy achieved was 3.48% higher than the original YOLOV5. The experimental results showed that the improved YOLOV5 model improved the recognition performance of the experimental dataset and effectively improved the network's ability to recognize the intima. Even in the case of small intra vascular differences and unclear neck boundaries, this provides new ideas for the research and application of medical image recognition.
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Liu et al. (2023) studied Carotid Atherosclerosis. Improved YOLOV5 model vs. Original YOLOV5 model was evaluated on Accuracy of plaque recognition (3.48% higher accuracy). An improved YOLOV5 model with custom anchor frames and attention mechanisms achieved a 3.48% higher accuracy in carotid artery ultrasound plaque recognition compared to the original YOLOV5.
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