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KMID : 1137820200410020094
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의공학회지
2020 Volume.41 No. 2 p.94 ~ p.100
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Classification of Anteroposterior/Lateral Images and Segmentation of the Radius Using Deep Learning in Wrist X-rays Images
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Lee Gi-Pyo
Kim Young-Jae
Lee Sang-Lim
Kim Kwang-Gi
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Abstract
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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.
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KEYWORD
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Distal radius fractures, Deep learning, Classification, Segmentation, X-rays
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FullTexts / Linksout information
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Listed journal information
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