Comparison of extraction of diffuse lung disease areas using CNN, FCN and U-Net

Abstract

In recent years, a lot of analytical methods of medical images using deep learning are suggested. Especially, convolutional neural network (CNN) is a model generally used in image recognition. When we classify diffuse lung disease (DLD) patterns using CNN, it is necessary to set region-of-interests (ROIs) on CT images. However, detection is important on diagnosis of DLD as same as classification. So, we propose a method to detect DLD opacities and extract DLD areas without setting ROIs. In this study, we evaluated detection methods of DLD areas using CNN, FCN and U-Net.