Lightweight YOLOv4 with multi-receptive fields for detection of pulmonary tuberculosis(PDF)
《中国医学物理学杂志》[ISSN:1005-202X/CN:44-1351/R]
- Issue:
- 2022年第9期
- Page:
- 1119-1127
- Research Field:
- 医学影像物理
- Publishing date:
Info
- Title:
- Lightweight YOLOv4 with multi-receptive fields for detection of pulmonary tuberculosis
- Author(s):
- WANG Jiahao; WANG Baozhu; GUO Zhitao; WANG Jinghua
- School of Electronic and Information Engineering, Hebei University of Technology, Tianjin 300401, China
- Keywords:
- Keywords: pulmonary tuberculosis YOLOv4 MobileNetv3 multi-receptive field
- PACS:
- R318；R521
- DOI:
- DOI:10.3969/j.issn.1005-202X.2022.09.011
- Abstract:
- Abstract: The characteristics of pulmonary tuberculosis are complex, with the high cost of manual screening, lack of standardized data sets. The current detection model based on convolution neural network has intricate structure, large number of parameters and detection accuracy needs to be further ameliorated. Therefore, an improved lightweight YOLOv4 model is proposed for pulmonary tuberculosis detection. A standardized dataset is constructed using 300 actual cases for evaluating the performance of the model. MobileNetv3 improved with residual channel attention module is used as the backbone extractor of YOLOv4 for further decreasing the number of parameters and fusing context information. Then the multi-receptive field module is added after the 3 effective feature layers of the backbone extractor, which effectively enhances the information extraction ability of the low feature layer and reduces the missed etection rate of small pulmonary tuberculosis lesions. The above improved modules were combined with the multi-scale structure of YOLOv4 to construct a lightweight YOLOv4 model with multi-receptive field for pulmonary tuberculosis detection.Compared with the original YOLOv4, the proposed model reduces the number of parameters of the model by about 47%, elevates the mAP value to 96.60%, and decreases the missed detection rate to 6%. It is verified that lightweight YOLOv4 with multi-receptive fields can effectively assist radiologists in the diagnosis of pulmonary tuberculosis.
Last Update: 2022-09-27