Presentation information

Oral presentation

General Session » [General Session] 2. Machine Learning

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

Tue. Jun 5, 2018 5:20 PM - 7:00 PM Room Z (3F Matsu Take)

座長:石畠 正和(NTT)

6:40 PM - 7:00 PM

[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 Kondo2,3, 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.