| Autor: |
Nan Chen, Zhichao Feng, Fei Li, Haibo Wang, Ruqin Yu, Jianhui Jiang, Lijuan Tang, Pengfei Rong, Wei Wang |
| Rok vydání: |
2022 |
| Předmět: |
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| Zdroj: |
Analytical methods : advancing methods and applications. 15(2) |
| ISSN: |
1759-9679 |
| Popis: |
Methods for automatic image analysis are demanded for dealing with the explosively increased imaging data in clinics. Osteoarthritis (OA) is a typical disease diagnosed based on X-ray imaging. Herein, we propose a novel modeling strategy based on YOLO version 3 (YOLOv3) for automatic simultaneous localization of knee joints and quantification of radiographic knee OA. As an advanced deep convolutional neural network (CNN) algorithm for target detection, YOLOv3 enables simultaneous small object detection and quantification due to its unique residual connection and feature map merging. Hence, a unified CNN model is built for the elegant integration of knee joint detection and corresponding OA severity grading using the YOLOv3 framework. We achieve desirable accuracy in knee OA grading using the public and clinical datasets. It provides improvements in the precision, recall |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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