基于迁移学习和卷积神经网络多网络颈动脉图像分类检测研究

doi:10.3969/j.issn.0258-8021.2024.03.002

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Abstract Carotid ultrasound is a main and convenient method for plaque diagnosis. Therefore, it is very important to obtain accurate information about plaque from ultrasound images for further clinical diagnosis. Due to the noise interference of ultrasonic machine and the difference of manual technical operation, the displayed section image is not clear and standard, which is easy to lead to false detection or missed detection of the plaque. In this work, a deep learning algorithm based on migration learning and CNN was proposed to realize the research of more accurate identification of carotid plaque. Firstly, 2591 longitudinal ultrasound images with clear and fuzzy carotid artery were selected to classify and control the lumen quality through ResNet network; After the lumen classification, 1114 longitudinal images with clear soft and hard plaque information were selected. The carotid lumen and plaque were classified and detected by RetinaNet network based on migration learning, and the comparative experiment was carried out by using Faster R-CNN and SSD network. For the lumen classification network, the classification accuracy of the test set was 93%. For the lumen and plaque classification detection network, 113 test set images were used to obtain the average accuracy of lumen detection, which reached 1 when the intersection union ratio (IOU) value was 0.5, 0.988 when the IOU value was 0.75, 0.838 when the IOU value was 0.5: 0.95, and the average recall rate reached 0.869, which were higher than those of Faster R-CNN and SSD networks; The average accuracy of hard plaque and soft plaque detection was 0.899 and 0.855 when IOU = 0.5, and the average recall was 0.609 and 0.578 respectively. Before the classification and recognition of carotid plaque, the quality classification control of carotid lumen image can effectively avoid the false detection of plaque caused by unclear image, improve the correctness of plaque detection, and is of great significance for the follow-up three-dimensional reconstruction of carotid artery.

Key words： carotid ultrasound images lumen quality classification control lumen detection plaque classification and recognition

Received: 04 March 2022

PACS:

R318

Corresponding Authors: ^* E-mail: liuzhi@sdu.edu.cn; chunwang@sdu.edu.cn

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	Articles by authors
	Sui Xiaoyu
	Han Jing
	Cao Yankun
	Mi Jia
	Song Yanyun
	Wang Jianlei
	Wang Chun
	Liu Zhi

Cite this article:

Sui Xiaoyu,Han Jing,Cao Yankun, et al. Research on Multi Network Carotid Artery Image Classification and Detection Based on TransferLearning and CNN[J]. Chinese Journal of Biomedical Engineering, 2024, 43(3): 267-277.

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http://cjbme.csbme.org/EN/10.3969/j.issn.0258-8021.2024.03.002 OR http://cjbme.csbme.org/EN/Y2024/V43/I3/267

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