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Related Experiment Video

Updated: Jun 30, 2025

A Methodological Approach to Non-invasive Assessments of Vascular Function and Morphology
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[A multiscale carotid plaque detection method based on two-stage analysis].

H Xiao1, W Fang1, M Lin1

  • 1School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China.

Nan Fang Yi Ke Da Xue Xue Bao = Journal of Southern Medical University
|March 19, 2024
PubMed
Summary
This summary is machine-generated.

A novel deep learning method, SM-YOLO, accurately identifies multiscale carotid plaques in ultrasound images. This two-stage approach improves detection speed and performance for real-time clinical applications.

Keywords:
YOLOXcarotid plaquedeep learningfeature fusionsupport vector machine

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Cardiovascular Diagnostics

Context:

  • Carotid artery plaques are crucial indicators of cardiovascular disease risk.
  • Accurate plaque identification in ultrasound images is challenging due to variations in scale and appearance.
  • Existing detection methods may lack the precision and speed required for clinical settings.

Purpose:

  • To develop and evaluate a robust deep learning-based method for accurate multiscale carotid plaque detection in ultrasound images.
  • To enhance the precision and efficiency of carotid plaque identification using a two-stage approach.
  • To compare the proposed method against established object detection models.

Summary:

  • A two-stage deep learning model, SM-YOLO, was developed for carotid plaque detection.
  • The first stage uses a YOLOX_l network with multiscale strategies for candidate plaque generation.
  • The second stage employs Histogram of Oriented Gradient (HOG) and Local Binary Pattern (LBP) features with a Support Vector Machine (SVM) classifier for refined detection.

Impact:

  • SM-YOLO demonstrated superior performance with 90.96% accuracy and 92.70% AP, outperforming other models.
  • The method achieves real-time detection capabilities, significantly faster than Faster R-CNN.
  • This advancement offers potential for improved diagnostic accuracy and efficiency in carotid ultrasound analysis.