1. University of Science and Technology of China, Hefei 230026, China 2. Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou 215163, China 3. The Central Hospital of Lishui City, Lishui 323000, China
A deep learning method based on DenseNet was proposed to distinguish between IAC and MIA from mixed ground glass nodules low-resolution CT images with 5 mm slice thickness, in order to classify histological subtypes of lung adenocarcinoma from low-dose CT images with low resolution. Samples were obtained from 105 low-resolution CT images with 5 mm slice thickness of 105 patients in the Central Hospital of Lishui City. The data was divided into training set and testing set. Then the training set was augmented; 2D and 3D DenseNet deep learning models were built to distinguish between IAC and MIA. The accuracy, sensitivity, specificity and the area under the receiver operating characteristic curve of the proposed 2D DenseNet method achieved 76.67%, 63.33%, 90.00% and 0.888 9, respectively, which was better than 3D DenseNet and other deep learning models. The deep learning method, especially the 2D DenseNet, may assist doctors in lung cancer screening to predict and guide histological subtypes of patients, which can quickly provide more accurate diagnosis results even under condition of low image resolution.
Jing YANG,Chen GENG,Hai-lin WANG,Jian-song JI,Ya-kang DAI. Classification on histological subtypes of lung adenocarcinoma from low-resolution CT images based on DenseNet. Journal of ZheJiang University (Engineering Science), 2019, 53(6): 1164-1170.
Tab.2Details of 2D and 3D DenseNet model architectures
Fig.5Comparison of performance of 2D and 3D DenseNet models
深度学习网络
a/%
e/%
p/%
AUC
注：括号内是与2D DenseNet相应性能指标相比下降的百分比
2D DenseNet
76.67
63.33
90.00
0.888 9
2D DenseNet-无颈瓶层
54.17（↓22.5）
20.84（↓42.49）
87.50（↓2.50）
0.592 6（↓0.296 3）
2D DenseNet-无弃权
58.33（↓18.34）
33.33（↓30.00）
83.33（↓6.67）
0.683 3（↓0.205 6）
2D DenseNet-无数据扩展
58.33（↓18.34）
63.33（↓0.00）
53.34（↓36.66）
0.694 4（↓0.194 5）
Tab.3Comparison of performance of different parameters in 2D DenseNet model
深度学习网络
a/%
e/%
p/%
AUC
LeNet
60.00
66.67
53.33
0.750 0
AlexNet
53.33
56.67
50.00
0.722 2
AgileCNN
53.33
63.33
43.33
0.638 9
Multi-channel CNN
56.66
63.33
50.00
0.777 8
2D DenseNet
76.67
63.33
90.00
0.888 9
Tab.4Comparison of performance of 2D DenseNet and other deep learning models
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