A fully automatic target detection and quantification strategy based on object detection convolutional neural network YOLOv3 for one-step X-ray image grading-Reference-Cited by-同舟云学术

A fully automatic target detection and quantification strategy based on object detection convolutional neural network YOLOv3 for one-step X-ray image grading

Published:2023 Issue:2 Volume:15 Page:164-170
ISSN:1759-9660
Container-title:Analytical Methods
language:en
Short-container-title:Anal. Methods

Author:

Chen Nan¹,Feng Zhichao²,Li Fei³,Wang Haibo¹,Yu Ruqin¹^ORCID,Jiang Jianhui¹^ORCID,Tang Lijuan¹,Rong Pengfei²^ORCID,Wang Wei²

Affiliation:

1. State Key Laboratory of Chemo/Biosensing and Chemometrics, College of Chemistry and Chemical Engineering, Hunan University, Changsha 410082, China

2. Department of Radiology, The Third Xiangya Hospital, Central South University, Changsha 410013, China

3. College of Electrical and Information Engineering, Hunan University, Changsha 410082, China

Abstract

A novel modeling strategy based on YOLO version 3 (YOLOv3) for automatic simultaneous localization of knee joints and quantification of radiographic knee OA.

Funder

National Natural Science Foundation of China

Publisher

Royal Society of Chemistry (RSC)

Subject

General Engineering,General Chemical Engineering,Analytical Chemistry

Link

http://pubs.rsc.org/en/content/articlepdf/2023/AY/D2AY01526A

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