A novel tuberculosis pathogens detection algorithm based on YOLOv8s

VernacularTitle:基于YOLOv8s的改进结核病病原体检测算法
Author: Zhen YUE ¹ ; Zhen SUN ; Ruiwen JU ; Qingdang LI
Author Information

1. 青岛科技大学信息科学技术学院,山东青岛 266061
Keywords: tuberculosis; deep learning; YOLOv8; attention mechanism
From: Chinese Journal of Medical Physics 2024;41(7):910-917
CountryChina
Language:Chinese
Abstract: To address the challenges of detecting tuberculosis pathogens in sputum smear images,such as complex backgrounds,small targets,and high costs of manual screening,a detection method based on YOLOv8s is presented.The structure is improved through spatial and channel reconstruction convolutions to limit feature redundancy,and a coordinate attention is introduced to expand the receptive field of the model.Furthermore,a spatial pyramid pooling cross-stage partial network is used to extract feature information at different scales,and a normalized attention mechanism is embedded to suppress less significant features.The experimental results on a public dataset show that compared with the original YOLOv8s model,the improved network model enhances precision and recall rates by 2.7%and 1.5%,respectively,and improved mean average precision at a confidence level of 0.5 by 2.3%,demonstrating that the improved model can effectively assist radiologists in diagnosis.