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

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 thro...

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Vydáno v:Multimedia tools and applications Ročník 80; číslo 20; s. 30399 - 30415
Hlavní autoři: Khamparia, Aditya, Gupta, Deepak, Rodrigues, Joel J. P. C., de Albuquerque, Victor Hugo C.
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York Springer US 01.08.2021
Springer Nature B.V
Témata:
Cancer
Cervical cancer
Classification
Computer Communication Networks
Computer Science
Data Structures and Information Theory
Deep learning
Diagnosis
Machine learning
Medical diagnosis
Multimedia Information Systems
Pap smear
Special Purpose and Application-Based Systems
Training
Deep learning
Variational
Convolution
Autoencoder
Cervical
ISSN:1380-7501, 1573-7721
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Shrnutí: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.
Bibliografie:ObjectType-Article-1
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ISSN:1380-7501
1573-7721
DOI:10.1007/s11042-020-09607-w