原著論文
深層学習を用いた前腕X線画像における腕の左右と向きの自動分類
2019 年 36 巻 2 号 p. 83-87
詳細
2019 年 36 巻 2 号 p. 83-87
The purpose of this paper is to develop a computerized classification method for right or left and directions of arms in forearm X-ray images using a deep convolutional neural network（DCNN）. 648 radiographs were obtained by using X-ray lower arm phantoms. These images were downsized to 213×256 pixels and used as training and test images in the DCNN. AlexNet and GoogLeNet were used as the DCNN. All radiographs were classified to eight categories by the DCNN. Classification accuracies were obtained by nine-fold cross validation tests. The accuracies using AlexNet and GoogLeNet were 79.3% and 92.6%, respectively. GoogLeNet would be useful to classify forearm radiographs automatically. The proposed method may contribute to quality assurance for medical images.
