Joint Multi-view Feature Network for Automatic Diagnosis of Pneumonia with CT Images

Automated recognition of pneumonia from chest CT plays an important role in the subsequent clinical treatment for patients. While a few pioneering works only focus on several random slices from chest CT image, thus they have ignored the anatomical dependency information of local lesions. Considering it, this paper explores a novel automatic classification method for pneumonia detection based on fusing regional and global information, which not only improves detection performance, but also provides explainable diagnostic basis for radiologists. Firstly, identifying the interested local region by a lesion detection module, then we extracts the correlation relationship between local regions through a graph attention module. The image-level classification results can be acquired by fusing the information of global and local region. To realize the detection of full CT sequence, a person-level classifier is designed in the proposed model. In the experiment, we collected 781 chest CT sequences in total corresponding to 274 cases of viral pneumonia patients, 285 cases of bacterial pneumonia patients and 222 cases of healthy people. The experimental results show that our model achieves the accuracy of 95.5%, with 95.6% precision and 0.991 AUC. The recall and F1 score are 95.8% and 95.7% respectively, which outperformed previous works. Therefore, our method can be regarded as an efficient assisted tool in the diagnose of pneumonia.