[1Z3] [General Session] 2. Machine Learning

[1Z3-05] Comparison of extraction of diffuse lung disease areas using CNN, FCN and U-Net

〇Kanako Murakami¹, Noriaki Hashimoto¹, Shoji Kido¹, Yasushi Hirano¹, Shingo Mabu¹, Kenji Kondo^2,3, Jun Ozawa² (1. Graduate school of Sciences and Technology for Innovation, Yamaguchi University, 2. Advanced Industrial Science and Technology, 3. Panasonic Corporation)

Keywords:Convolutional Neural Network, Fully Convolutional Network, U-Net, Diffuse Lung Disease

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.