Research on High-resolution Mycobacterium Tuberculosis Identification Method Based on YOLOv7 Network
Tuberculosis is one of the ten leading causes of death worldwide.To address the complex background and small target size of Mycobacterium tuberculosis in sputum smear images,in this paper,we put forward a YOLOv7 detection network for detecting Mycobac-terium tuberculosis using sputum smear images.In the backbone network,CBAM attention modules are embedded after efficient aggre-gation networks at different scales to extract features at different scales.We introduce separation and merging operations in neck net-works to improve MPConv structure and reduce image feature loss due to convolutional downsampling in deep networks,and introduce Gaussian distribution distances in the head mesh to avoid small target misses due to overlapping bounding boxes.This technique is ex-ceedingly precise and rapid,rendering it ideal for identifying Mycobacterium tuberculosis.Experiments indicate that the mean average accuracy achieved by the improved model is 87.8％,a 5.1％enhancement compared to the baseline network model,and better than similar algorithms,which is important for advancing intelligent detection of Mycobacterium tuberculosis.
Mycobacterium tuberculosis detectionYOLOv7attention mechanismseparation and merger operationsGaussian distribu-tion
万方数据
维普
