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http://dx.doi.org/10.9718/JBER.2020.41.2.94

Classification of Anteroposterior/Lateral Images and Segmentation of the Radius Using Deep Learning in Wrist X-rays Images  

Lee, Gi Pyo (Department of Biomedical Engineering, College of Health Science, Gachon University)
Kim, Young Jae (Department of Biomedical Engineering, College of Health Science, Gachon University)
Lee, Sanglim (Department of Orthopedic Surgery, Inje University Sanggye Paik Hospital)
Kim, Kwang Gi (Department of Biomedical Engineering, College of Health Science, Gachon University)
Publication Information
Journal of Biomedical Engineering Research / v.41, no.2, 2020 , pp. 94-100 More about this Journal
Abstract
The purpose of this study was to present the models for classifying the wrist X-ray images by types and for segmenting the radius automatically in each image using deep learning and to verify the learned models. The data were a total of 904 wrist X-rays with the distal radius fracture, consisting of 472 anteroposterior (AP) and 432 lateral images. The learning model was the ResNet50 model for AP/lateral image classification, and the U-Net model for segmentation of the radius. In the model for AP/lateral image classification, 100.0% was showed in precision, recall, and F1 score and area under curve (AUC) was 1.0. The model for segmentation of the radius showed an accuracy of 99.46%, a sensitivity of 89.68%, a specificity of 99.72%, and a Dice similarity coefficient of 90.05% in AP images and an accuracy of 99.37%, a sensitivity of 88.65%, a specificity of 99.69%, and a Dice similarity coefficient of 86.05% in lateral images. The model for AP/lateral classification and the segmentation model of the radius learned through deep learning showed favorable performances to expect clinical application.
Keywords
Distal radius fractures; Deep learning; Classification; Segmentation; X-rays;
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