Research on detection method of Tubercle Bacilli based on the improved YOLOv5

Abstract

Abstract In the field of medical imaging, the detection method of Tubercle Bacilli based on deep learning can overcome the shortcomings of traditional manual detection methods, such as large subjectivity, large workload, slow detection speed, and reduce the occurrence of false detection or missed detection under specific circumstances. However, due to the small target and complex background of Tubercle Bacilli, the detection results are still not accurate enough. In order to reduce the influence of sputum sample background on Tubercle Bacilli detection and improve the accuracy of the model for Tubercle Bacilli detection, a target detection algorithm YOLOv5-CTS based on YOLOv5 algorithm is proposed in this paper. The algorithm first integrates the CTR3 module at the bottom of Backbone of YOLOv5 network to obtain more high-quality feature information, which brings significant performance improvement to the model; then in the neck and head part, a hybrid model with the improved feature pyramid networks and the added large-scale detection layer is utilized to perform feature fusion and small target detection; finally, the SCYLLA-Intersection over Union loss function is integrated. The experimental results show that YOLOv5-CTS increases the mean average precision to 86.2% compared with the existing target detection algorithms for Tubercle Bacilli, such as Faster R-CNN, SSD and RetinaNet, etc, which shows the effectiveness of this method.