Searching for just a few words should be enough to get started. If you need to make more complex queries, use the tips below to guide you.
Article type: Research Article
Authors: Zhou, Siboa | Qiu, Yuxuanb | Han, Lina; c | Liao, Guolianga | Zhuang, Yana | Ma, Buyunb | Luo, Yanb | Lin, Jianglia | Chen, Kea; *
Affiliations: [a] College of Biomedical Engineering, Sichuan University, Chengdu, China | [b] Department of Ultrasound, West China Hospital, Sichuan University, Chengdu, China | [c] Highong Intellimage Medical Technology (Tianjin) Co., Ltd, Tianjin, China
Correspondence: [*] Corresponding author: Ke Chen, College of Biomedical Engineering, Sichuan University, Chengdu, 610065, China. Tel.: +86 028 85416050; E-mail: chenke@scu.edu.cn.
Abstract: BACKGROUND:The intelligent diagnosis of thyroid nodules in ultrasound image is an important research issue. Automatically locating the region of interest (ROI) of thyroid nodules and providing pre-diagnosis results can help doctors to diagnose faster and more accurate. OBJECTIVES:This study aims to propose a model, which can detect multiple nodules stably and accurately in order to avoid missed detection and misjudgment. In addition, the detection speed of the model needs to be fast for real-time diagnosis in ultrasound images. METHODS:Based on the object detection technology, we propose an accurate, robust and high-speed network with multiscale fusion strategy called Efficient-YOLO, which can realize the localization and recognition of nodules at the same time. Finally, multiple metrics are used to measure the diagnostic ability of the model. RESULTS:Experimental results conducted on 3,562 ultrasound images show that our new model greatly increases the accuracy and speed of the detection compared with the baseline model. The best mAP is 92.64%, and the fastest detection speed is 45.1 frames per second. CONCLUSIONS:This study proposed an effective method to diagnosis thyroid nodules automatically, which can meet the real-time requirements, indicating that its effectiveness and feasibility for future clinical application.
Keywords: Thyroid nodules, object detection, generalization, real-time detection, YOLO
DOI: 10.3233/XST-221206
Journal: Journal of X-Ray Science and Technology, vol. 30, no. 5, pp. 967-981, 2022
IOS Press, Inc.
6751 Tepper Drive
Clifton, VA 20124
USA
Tel: +1 703 830 6300
Fax: +1 703 830 2300
sales@iospress.com
For editorial issues, like the status of your submitted paper or proposals, write to editorial@iospress.nl
IOS Press
Nieuwe Hemweg 6B
1013 BG Amsterdam
The Netherlands
Tel: +31 20 688 3355
Fax: +31 20 687 0091
info@iospress.nl
For editorial issues, permissions, book requests, submissions and proceedings, contact the Amsterdam office info@iospress.nl
Inspirees International (China Office)
Ciyunsi Beili 207(CapitaLand), Bld 1, 7-901
100025, Beijing
China
Free service line: 400 661 8717
Fax: +86 10 8446 7947
china@iospress.cn
For editorial issues, like the status of your submitted paper or proposals, write to editorial@iospress.nl
如果您在出版方面需要帮助或有任何建, 件至: editorial@iospress.nl