基于Cascade Rcnn的超声甲状腺结节检测研究

doi:10.3969/j.issn.0258-8021.2022.01.008

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Abstract Thyroid ultrasound images have low contrast, unclear edges, high noise, and the surrounding tissues are complex and difficult to distinguish, making it extremely difficult for doctors to diagnose thyroid diseases. To overcome this problem, Cascade Rcnn target detection algorithm was used in this work, with ResNet50, Resnet101 and fusion compression incentive attention modules SE-ResNet50, SE-ReNet101 as the backbone network. There were 1 513 cases thyroid ultrasound images (including 832 cases benign nodules and 681 cases malignant nodules) obtained from a third-class hospital. Under the guidance of professional sonographers, the data were preprocessed into the standard coco format data set. The weights obtained from the pre-training of the large Imagenet database by transfer learning were migrated to this experimental model structure. Comparing with the experimental results of the four backbone networks, Cascade Rcnn algorithm with SE-ResNet101 as the backbone network achieved an accuracy of 92.4%, recall rate of 86.2%, specificity of 95.1%1, F1 value of 89.22%, and mAP value of 82.4%. The detection result of nodule localization and classification of benign and malignant was of clinical guiding significance for assisting doctors in the diagnosis of thyroid ultrasound images.

Key words： thyroid ultrasound image Cascade Rcnn target detection transfer learning

Received: 31 December 2020

PACS:

R318

Corresponding Authors: ^* E-mail: haowiezh@aliyun.com

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	Articles by authors
	Zhang Haowei
	Li Zhanqi
	Liu Ying
	Li Miao

Cite this article:

Zhang Haowei,Li Zhanqi,Liu Ying, et al. Study of Ultrasonic Thyroid Nodules Detection Based on Cascade Rcnn[J]. Chinese Journal of Biomedical Engineering, 2022, 41(1): 64-72.

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http://cjbme.csbme.org/EN/10.3969/j.issn.0258-8021.2022.01.008 OR http://cjbme.csbme.org/EN/Y2022/V41/I1/64

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