Automatic Diagnosis of COPD in Lung CT Images based on Multi-View DCNN

Chronic obstructive pulmonary disease (COPD) has long been one of the leading causes of morbidity and mortality worldwide. Numerous studies have shown that CT image analysis is an effective way to diagnose patients with COPD. Automatic diagnosis of CT images using computer vision will shorten the time a patient takes to confirm COPD. This enables patients to receive timely treatment. CT images are three-dimensional data. The extraction of 3D texture features is the core of classification problem. However, the classification accuracy of the current computer vision models is still not high when extracting these features. Therefore, computer vision assisted diagnosis has not been widely used. In this paper, we proposed MV-DCNN, a multi-view deep neural network based on 15 directions. The experimental results show that compared with the state-of-art methods, this method significantly improves the accuracy of COPD classification, with an accuracy of 97.7%. The model proposed here can be used in the medical institutions for diagnosis of COPD.