Malignancy Grading of Lung Nodules Based on CT Signs Quantization Analysis

Hao CHEN; Hongbai DUAN; Ziyuan GUO; Yongqian QIANG

doi:10.11999/JEIT200167

Volume 43 Issue 5

May 2021

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Article Navigation > Journal of Electronics & Information Technology > 2021 > 43(5): 1405-1413

Hao CHEN, Hongbai DUAN, Ziyuan GUO, Yongqian QIANG. Malignancy Grading of Lung Nodules Based on CT Signs Quantization Analysis[J]. Journal of Electronics & Information Technology, 2021, 43(5): 1405-1413. doi: 10.11999/JEIT200167

Citation:

Hao CHEN, Hongbai DUAN, Ziyuan GUO, Yongqian QIANG. Malignancy Grading of Lung Nodules Based on CT Signs Quantization Analysis[J]. Journal of Electronics & Information Technology, 2021, 43(5): 1405-1413. doi: 10.11999/JEIT200167

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Malignancy Grading of Lung Nodules Based on CT Signs Quantization Analysis

doi: 10.11999/JEIT200167

1.
School of Computer, Xi`an University of Posts & Telecommunications, Xi’an 710121, China
2.
Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing, Xi’an University of Post and Telecommunications, Xi’an 710121, China
3.
First Affiliated Hospital of Xi`an Jiaotong University, Xi’an 710061, China

Funds: The National Natural Science Foundation of China (61876138, 61203311), The Natural Science Basic Research Program of Shaanxi Province (2019JM-365), The Scientific Research Program Funded by Shaanxi Provincial Education Department (17JK0701), The Science Foundation of the Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing (XUPT-KLND (201804)), The Innovation Funds of Xi'an University of Posts and Telecommunications (CXJJLI2018017)

Received Date: 2020-03-13
Rev Recd Date: 2020-09-25

Available Online: 2020-10-16

Publish Date: 2021-05-18

Abstract

Abstract

In order to improve the accuracy and interpretability of the grading of malignant nodules in the lung, a method is proposed to achieve grading automatically for lung nodules by using (Computed Tomography, CT) signs. Firstly, features sets are extracted of CT signs by combing the radiomics features with the higher-order features extracted by convolutional neural network. Then, the ensemble classifier is optimized by the evolutionary search mechanism based on the mixed feature sets, and it is used to realize quantitative scores for 7 CT signs. Finally, 7 quantitative scores are input to the optimized multi-classifier to achieve the grading of malignant nodules in the lung. In the experience, 2000 samples of lung nodules in LIDC-IDRI data set are used to train and test the proposed method. The results show that the recognition accuracy of the 7 CT signs can reach more than 0.9642, the grading accuracy reaches 0.8618, the precision reaches 0.8678, the recall reaches 0.8617, and the F1 index reaches 0.8627. With respect to typical algorithms, the proposed method not only has high accuracy, but also can quantitatively analyze the CT signs that make the grade result of malignancy more interpretive.
- Lung malignancy grading,
- Computed Tomography (CT) signs,
- Evolutionary ensemble learning,
- Quantization analysis,
- Interpretability

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References(24)

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