典型文献
Cancer classification with data augmentation based on generative adversarial networks
文献摘要：
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 inade-quate 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.
文献关键词：
中图分类号：
作者姓名：
Kaimin WEI;Tianqi LI;Feiran HUANG;Jinpeng CHEN;Zefan HE
作者机构：
College of Information Science and Technology,Jinan University,Guangzhou 510632,China;Guangdong Key Laboratory of Data Security and Privacy Protection,Guangzhou 510632,China;School of Software Engineering,Beijing University of Posts and Telecommunications,Beijing 100876,China
文献出处：
引用格式：
[1]Kaimin WEI;Tianqi LI;Feiran HUANG;Jinpeng CHEN;Zefan HE-.Cancer classification with data augmentation based on generative adversarial networks)[J].计算机科学前沿,2022(02):64-74
A类：
B类：
Cancer,classification,data,augmentation,generative,adversarial,networks,Accurate,diagnosis,significant,step,cancer,treatment,Machine,learning,support,doctors,prognosis,decision,making,its,performance,always,weakened,by,small,quantity,genetic,Fortunately,deep,effectively,process,dimensional,growing,However,problem,inade,quate,remains,unsolved,has,lowered,To,end,propose,model,that,uses,target,help,generator,training,We,reconstruction,loss,further,stabilize,improve,quality,generated,samples,also,present,optimize,Experimental,results,mean,absolute,error,made,our,than,DC,GAN,accuracy,produced,higher,created,reaches,which,without,any
AB值：
0.554501
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