부산대학교 도서관

Chest X-ray is the most common diagnostic imaging method for pneumothorax. When the volume of pneumothorax is large, a drainage tube needs to be placed for treatment. In view of the vigorous development of deep learning in the field of medical imaging in recent years, this study will use transfer learning to train a convolutional neural network model for automatical screening of pneumothorax patients who may require immediate intervention. A total of 6529 poster-anterior chest X-rays were retrospectively included, including 993 cases of pneumothorax and 5536 cases of non-pneumothorax, ranging in age from 12 to 104 years old. The data set is divided into training, validation, and testing sub-data sets as 8:1:1. Using the Inception-v4 model, the accuracy rate of the validation sub-data set is 98.77%, the accuracy rate of the testing sub-data set is 97.78%, and the area under the curve is 0.99. There were 140 true positive cases, 1093 true negative cases, 8 false positive cases, and 20 false negative cases in the testing sub-data set. The precision is 0.95, the recall is 0.88, and the F1 score is 0.91. This study shows that the classification artificial intelligence model trained with local image data is state-of-the-art. This model may be used as a tool for preliminary screening or auxiliary diagnosis to assist doctors in the interpretation of images, and can be used to improve the treatment process, achieve early notification, timely treatment, and improve patient safety.