CT Image Detection of Pulmonary Tuberculosis Based on the Improved Strategy YOLOv5

Jing Liu, Haojie Xie, Mingli Lu, Ye Li, Bing Wang, Zhaogang Sun, Wei He, Limin Wen, Dailun Hou

OAI: oai:igi-global.com:329217 • DOI: 10.4018/IJSIR.329217

The diagnosis of pulmonary tuberculosis is a complicated process with a long wait. According to the WS 288-2017 standard, PTB can be divided into five types of imaging. To date, no relevant studies on PTB CT images based on the Yolov5 algorithm have been retrieved. To develop an improved strategy YOLOv5, for the classification of PTB lesions based on whole, CT slices were combined with three other modules. CT slices of PTB collected from hospitals were set as training, verification, and external test sets. It is compared with YOLOv5, SSD and RetinaNet neural network methods. The values of precision, recall, MAP, and F1-score of the improved strategy YOLOv5 for the external test were 0.707, 0.716, 0.715, and 0.71. In this study, based on the same dataset, the improved strategy YOLOv5 model has better results than other networks. Our method provides an effective method for the timely detection of PTB.

Computer Science and Information Technology Swarm Intelligence Computational Intelligence