A fully automatic target detection and quantification strategy based on object detection convolutional neural network YOLOv3 for one-step X-ray image grading.
Nan ChenZhichao FengFei LiHaibo WangRu-Qin YuJian-Hui JiangLijuan TangPengfei RongWei WangPublished in: Analytical methods : advancing methods and applications (2023)
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, F 1 score and diagnostic accuracy of knee OA as well. Because of the fully automatic target detection and quantification, the time of handling an image is merely 40 ms from inputting the image to getting its label, supporting quick clinic decisions. It, thus, affords convenient and efficient image analysis for daily clinical diagnosis.
Keyphrases
- deep learning
- convolutional neural network
- knee osteoarthritis
- total knee arthroplasty
- loop mediated isothermal amplification
- high resolution
- real time pcr
- label free
- artificial intelligence
- primary care
- healthcare
- rheumatoid arthritis
- mental health
- working memory
- computed tomography
- physical activity
- magnetic resonance
- rna seq
- high density