부산대학교 도서관

In emergency medicine, imaging diagnosis by un-enhanced computed tomography (CT) is frequently used due to the remarkable progress and spread of CT. In the case of acute diseases such as trauma, accurate diagnosis of the upper abdominal region may be difficult depending on the experience of the doctor who interprets the images and the display conditions. In this study, we propose a method using deep convolutional neural network (DCNN) to automatically classify the presence or absence of traumatic hematoma in coronal images reconstructed from plain CT images. Coronal images are useful for observing a wide area of the upper abdomen and are often used. 337 images with traumatic hematoma and 492 images without were divided into 8 data sets. Finally, 17 types of DCNN were used, and the images were output into two categories, with and without traumatic hematoma, by 8-fold cross-validation. Receiver operatorating characteristic (ROC) analysis was performed to calculate accuracy and area under the curve (AUC). The highest accuracy was 0.841 and AUC was 0.909 when DenceNet-201 was used.