A review of deep learning methods for the detection and classification of pulmonary nodules.
10.7507/1001-5515.201903027
- Author:
Qingyi ZHAO
1
,
2
;
Ping KONG
3
;
Jianzhong MIN
2
;
Yanli ZHOU
2
;
Zhuangzhuang LIANG
4
;
Sheng CHEN
4
;
Maoju LI
2
Author Information
1. School of Medical Instrument and Food Engineering, University of Shanghai for Science & Technology, Shanghai 200093, P.R.China
2. Shanghai Key Laboratory of Molecular Imaging, Shanghai University of Medicine & Health Sciences, Shanghai 201318, P.R.China.
3. Shanghai Key Laboratory of Molecular Imaging, Shanghai University of Medicine & Health Sciences, Shanghai 201318, P.R.China.kongp@sumhs.edu.cn.
4. School of Optical Electrical and Computer Engineering, University of Shanghai for Science & Technology, Shanghai 200093, P.R.China.
- Publication Type:Journal Article
- Keywords:
computer-aided diagnosis;
convolutional neural network;
deep learning;
medical image;
pulmonary nodules
- MeSH:
Deep Learning;
Humans;
Multiple Pulmonary Nodules;
Neural Networks, Computer;
Solitary Pulmonary Nodule;
Tomography, X-Ray Computed
- From:
Journal of Biomedical Engineering
2019;36(6):1060-1068
- CountryChina
- Language:Chinese
-
Abstract:
Lung cancer has the highest mortality rate among all malignant tumors. The key to reducing lung cancer mortality is the accurate diagnosis of pulmonary nodules in early-stage lung cancer. Computer-aided diagnostic techniques are considered to have potential beyond human experts for accurate diagnosis of early pulmonary nodules. The detection and classification of pulmonary nodules based on deep learning technology can continuously improve the accuracy of diagnosis through self-learning, and is an important means to achieve computer-aided diagnosis. First, we systematically introduced the application of two dimension convolutional neural network (2D-CNN), three dimension convolutional neural network (3D-CNN) and faster regions convolutional neural network (Faster R-CNN) techniques in the detection of pulmonary nodules. Then we introduced the application of 2D-CNN, 3D-CNN, multi-stream multi-scale convolutional neural network (MMCNN), deep convolutional generative adversarial networks (DCGAN) and transfer learning technology in classification of pulmonary nodules. Finally, we conducted a comprehensive comparative analysis of different deep learning methods in the detection and classification of pulmonary nodules.
