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 and Dailun Hou
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Jing Liu: Beijing Chest Hospital, Capital Medical University, China & The Fifth People's Hospital of Suzhou, China
Haojie Xie: Changshu Institute of Technology, China
Mingli Lu: Changshu Institute of Technology, China
Ye Li: Beijing Chest Hospital, Capital Medical University, China
Bing Wang: Beijing Chest Hospital, Capital Medical University, China
Zhaogang Sun: Beijing Tuberculosis and Thoracic Tumor Research Institute, China
Wei He: Beijing Chest Hospital, Capital Medical University, China
Limin Wen: Infectious Disease Hospital of Heilongjiang Province, China
Dailun Hou: Beijing Chest Hospital, Capital Medical University, China

International Journal of Swarm Intelligence Research (IJSIR), 2023, vol. 14, issue 1, 1-12

Abstract: 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.

Date: 2023
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