Multi-Convolutional Neural Network for Detection of Pulmonary Tuberculosis on Chest X-Ray.

Due to the small difference between the focus area of pulmonary tuberculosis and the normal lung area, it is difficult to accurately detect the pulmonary tuberculosis disease. To solve this problems, a tuberculosis disease detection algorithm based on the combination of deep separable convolution and graph convolution is proposed. Firstly, the depth separable convolution module is used to extract the local features of the image. Secondly, the graph convolution module is used to obtain the global features of the image. Then, using a shortcut branching operation, the extracted local and global features are fused. Finally, the fused features are output through the linear layer to output the detection results. The algorithm model has been fully experimented and verified on the publicly available positive chest X-Ray dataset collected by the Third Hospital of Shenzhen City, Guangdong Province, China. Experimental results show that compared with the benchmark model, the proposed algorithm model improves by 2.98, 3.23, 2.94 and 3.08 percentage points on the four evaluation indicators of Accuracy, Precision, Recall and F1-Score, respectively, which proves the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]