Cancer classification with data augmentation based on generative adversarial networks

doi:10.1007/s11704-020-0025-x

Front. Comput. Sci.

2022, Vol. 16

Issue (2) : 162601 https://doi.org/10.1007/s11704-020-0025-x

RESEARCH ARTICLE

Cancer classification with data augmentation based on generative adversarial networks

Kaimin WEI^1,², Tianqi LI^1,², Feiran HUANG^1,²(

), Jinpeng CHEN³, Zefan HE^1,²

¹. College of Information Science and Technology, Jinan University, Guangzhou 510632, China
². Guangdong Key Laboratory of Data Security and Privacy Protection, Guangzhou 510632, China
³. School of Software Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China

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Abstract

Accurate diagnosis is a significant step in cancer treatment. Machine learning can support doctors in prognosis decision-making, and its performance is always weakened by the high dimension and small quantity of genetic data. Fortunately, deep learning can effectively process the high dimensional data with growing. However, the problem of inadequate data remains unsolved and has lowered the performance of deep learning. To end it, we propose a generative adversarial model that uses non target cancer data to help target generator training. We use the reconstruction loss to further stabilize model training and improve the quality of generated samples. We also present a cancer classification model to optimize classification performance. Experimental results prove that mean absolute error of cancer gene made by our model is 19.3% lower than DC-GAN, and the classification accuracy rate of our produced data is higher than the data created by GAN. As for the classification model, the classification accuracy of our model reaches 92.6%, which is 7.6% higher than the model without any generated data.

Keywords data mining cancer data analysis deep learning generative adversarial networks

Corresponding Author(s): Feiran HUANG

Just Accepted Date: 27 April 2020 Online First Date: 31 August 2021 Issue Date: 03 September 2021

Cite this article:

Kaimin WEI,Tianqi LI,Feiran HUANG, et al. Cancer classification with data augmentation based on generative adversarial networks[J]. Front. Comput. Sci., 2022, 16(2): 162601.

URL:

https://academic.hep.com.cn/fcs/EN/10.1007/s11704-020-0025-x
https://academic.hep.com.cn/fcs/EN/Y2022/V16/I2/162601

Fig.1 The structure of the generative adversarial model for cancer gene expression data

Fig.2 The structure of cancer classification model based on generated data

Fig.3 The difference between real samples and generated ones

Fig.4 An image and its corresponding label

Fig.5 A generated image sample with its label vector

Tab.1 The statistics of cancer datasets

Tab.2 The MAE of different generative models

Fig.6 The convergence of different generative models

Tab.3 Classification results of different generative models

Tab.4 Classification results for different classifiers

Fig.7 The ROC curve of different classifiers

Label smooth rate	Accuracy
Label smooth rate	Mean	SD
$? = 1$	0.889	0.025
$? = 0.8$	0.899	0.022
$? = 0.6$	0.912	0.021
$? = 0.4$	0.926	0.008
$? = 0.2$	0.919	0.017
$? = 0$	0.892	0.046

Tab.5 Accuracy results under different label smooth rate

Fig.8 The ROC curve under different smooth rate

Tab.6 Results with different quantities of amplified data

Fig.9 The ROC curve with different quantities of amplified data

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