DCAVN: Cervical cancer prediction and classification using deep convolutional and variational autoencoder network

被引:0
作者
Aditya Khamparia
Deepak Gupta
Joel J. P. C. Rodrigues
Victor Hugo C. de Albuquerque
机构
[1] Lovely Professional University,School of Computer Science and Engineering
[2] Maharaja Agrasen Institute of Technology,Graduate Program in Applied Informatics
[3] Federal University of Piaui,undefined
[4] Instituto de Telecommunicações,undefined
[5] University of Fortaleza,undefined
来源
Multimedia Tools and Applications | 2021年 / 80卷
关键词
Variational; Convolution; Cervical; Deep learning; Autoencoder;
D O I
暂无
中图分类号
学科分类号
摘要
Early detection, early diagnosis and classification of the cancer type facilitates faster disease management of patients. Cervical cancer is fourth most pervasive cancer type which affects life of many people worldwide. The intent of this study is to automate cancer diagnosis and classification through deep learning techniques to ensure patients health condition progress timely. For this research, Herlev dataset was utilized which contains 917 benchmarked pap smear cells of cervical with 26 attributes and two target variables for training and testing phase. We have adopted combination of convolutional network with variational autoencoder for data classification. The usage of variational autoencoder reduces the dimensionality of data for further processing with involvement of softmax layer for training. The results have been obtained over 917 cancerous image type pap smear cells, where 70% (642) allocated for training and remaining 30% (275) considered for test data set. The proposed architecture achieved variational accuracy of 99.2% with 2*2 filter size and 99.4% with 3*3 filter size using different epochs. The proposed hybrid variational convolutional autoencoder approach applied first time for cervical cancer diagnosis and performed better than traditional machine learning methods.
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页码:30399 / 30415
页数:16
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