Automatic Identification of Osteoporosis from Phalanges Computed Radiography Images Based on CNN

Abstract

Osteoporosis is the main disease of bone. Although image diagnosis for osteoporosis is effective, there are concerns about increased burdens on doctors and variations in diagnostic results due to experience differences of doctors and undetected lesions. Therefore, in this paper, we propose a diagnostic support method to classify osteoporosis from Computed Radiography (CR) images of the phalanges and present classification results to doctors. In the proposed method, we constructed classifiers using Residual Network (ResNet), which is one type of convolution neural network, and classified the presence or absence of osteoporosis. For the input image to ResNet, we used the image generated from CR images. In this paper, we proposed three kinds of input images and conducted training and classification evaluation on each image. In the experiment, the proposed method was applied to 101 cases and evaluated using the Area Under the Curve (AUC) value on the Receiver Operating Characteristics (ROC) curve, the maximum value of which was 0.931.