Optimization of CNN for Diagnosis on Lung Disease by Lung Segmentation and Rib Suppression

Xinye Yang

Abstract: The discrimination of lung diseases by chest X- ray images is a clinically important tool. How to use artificial intelligence to accurately and quickly help doctors to diagnose different lung diseases is very important in the context of the current COVID-19 global pandemic. In this paper, we propose a model structure, including two U-Net, which implement lung segmentation and rib suppression for chest X-ray images respectively, image enhancement techniques such as histogram equalization, which enhances images contrast, and a Xception- based CNN, which classifies the processed images finally. The model can effectively avoid the interference of regions outside the lung to CNN for feature recognition and the influence of environmental factors such as X-ray machines on the quality of X-ray images and thus on the classification. The experimental results show that the classification accuracy of the model is higher than that of the direct use of the Xception model for classification.