Diagnosis Algorithm of Pulmonary Nodules Malignancy Based on Generative Adversarial Network

doi:10.12068/j.issn.1005-3026.2022.01.004

Abstract

Abstract: To solve the problem of data scarcity and expensive costs in manual labeling of CT images of solid pulmonary nodules， a computer aided diagnosis algorithm for classification between lung tuberculosis and lung adenocarcinoma of solid pulmonary nodules by combining generative adversarial network and ensemble learning was proposed. Firstly， the original CT image dataset was augmented using Wasserstein generative adversarial network with gradient penalty(WGAN-GP)， in order to relieve the problem of overfitting caused by small-scale dataset and class imbalance. Then， feature extraction was performed using convolutional neural network， followed by a dimension reduction procedure using principal component analysis(PCA). Finally， deep features concatenated with effective subjective features were classified by an ensemble learning model to give final prediction of the patient.Analysis based on multi-center clinical data indicated that the proposed algorithm has better performance compared to traditional convolutional neural network method.

Key words: solid pulmonary nodules; lung adenocarcinoma; computed tomography(CT); generative adversarial network(GAN); ensemble learning

CLC Number:

TP391.41

LUO Jia-jian， FENG Bao， CHEN Xiang-meng， GU Zheng-hui. Diagnosis Algorithm of Pulmonary Nodules Malignancy Based on Generative Adversarial Network[J]. Journal of Northeastern University(Natural Science), 2022, 43(1): 24-32.

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