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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| Published in: | Multimedia tools and applications Vol. 80; no. 20; pp. 30399 - 30415 |
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| Main Authors: | , , , |
| Format: | Journal Article |
| Language: | English |
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Springer US
01.08.2021
Springer Nature B.V |
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| ISSN: | 1380-7501, 1573-7721 |
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| Abstract | 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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| AbstractList | 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. |
| Author | de Albuquerque, Victor Hugo C. Khamparia, Aditya Gupta, Deepak Rodrigues, Joel J. P. C. |
| Author_xml | – sequence: 1 givenname: Aditya surname: Khamparia fullname: Khamparia, Aditya email: aditya.khamparia88@gmail.com organization: School of Computer Science and Engineering, Lovely Professional University – sequence: 2 givenname: Deepak surname: Gupta fullname: Gupta, Deepak organization: Maharaja Agrasen Institute of Technology – sequence: 3 givenname: Joel J. P. C. surname: Rodrigues fullname: Rodrigues, Joel J. P. C. organization: Federal University of Piaui, Instituto de Telecommunicações – sequence: 4 givenname: Victor Hugo C. surname: de Albuquerque fullname: de Albuquerque, Victor Hugo C. organization: Graduate Program in Applied Informatics, University of Fortaleza |
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| Cites_doi | 10.1016/j.procs.2017.09.044 10.1016/j.eswa.2003.12.005 10.1016/j.eswa.2018.08.050 10.31557/APJCP.2018.19.12.3571 10.1016/j.eswa.2018.03.053 10.1109/ACCESS.2017.2763984 10.1007/s11517-013-1108-8 10.1002/sam.11261 10.1016/S1098-3597(00)90019-X 10.1109/TNN.2011.2130540 10.3390/rs10010110 10.1109/JBHI.2017.2705583 10.1016/j.mefs.2016.09.002 10.4249/scholarpedia.1717 10.23919/FRUCT.2017.8071332 |
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| Keywords | Deep learning Variational Convolution Autoencoder Cervical |
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| References | Chen, Gou, Wang, Li, Jiao (CR3) 2018; 10 Shao, Zhang, Wang, Deng (CR15) 2011; 22 CR6 Zhang, Lu, Nogues, Summers, Liu, Yao, Deeppap (CR20) 2017; 21 Ho, Jee, Lee, Park (CR8) 2004; 27 CR9 Ferlay, Soerjomataram, Ervik, Forman, Bray, Dixit (CR5) 2012 CR13 Wu, Zhou (CR18) 2017; 5 Kusy, Obrzut, Kluska (CR12) 2013; 51 Almubarak, Stanley, Long, Antani, Thoma, Zuna, Frazier (CR2) 2017; 114 CR11 CR10 Richhariya, Tanveer (CR14) 2018; 15 Elayaraja, Suganthi (CR4) 2018; 19 Goodman (CR7) 2000; 3 Verma, Verma, Vashist, Attri, Singhal (CR17) 2017; 22 Adem, Kilicarslan, Cömert (CR1) 2019; 115 Yamal, Guillaud, Atkinson, Follen, MacAulay, Cantor, Cox (CR19) 2015; 8 Sun, Li, Cao, Lang (CR16) 2017; 13 9607_CR9 YH Shao (9607_CR15) 2011; 22 W Chen (9607_CR3) 2018; 10 9607_CR6 B Richhariya (9607_CR14) 2018; 15 J Ferlay (9607_CR5) 2012 A Verma (9607_CR17) 2017; 22 W Wu (9607_CR18) 2017; 5 P Elayaraja (9607_CR4) 2018; 19 SH Ho (9607_CR8) 2004; 27 HA Almubarak (9607_CR2) 2017; 114 9607_CR13 G Sun (9607_CR16) 2017; 13 L Zhang (9607_CR20) 2017; 21 9607_CR10 9607_CR11 M Kusy (9607_CR12) 2013; 51 K Adem (9607_CR1) 2019; 115 A Goodman (9607_CR7) 2000; 3 JM Yamal (9607_CR19) 2015; 8 |
| References_xml | – volume: 114 start-page: 281 year: 2017 end-page: 287 ident: CR2 article-title: Convolutional neural network based localized classification of uterine cervical cancer digital histology images publication-title: Procedia Comput Sci doi: 10.1016/j.procs.2017.09.044 – volume: 27 start-page: 97 issue: 1 year: 2004 end-page: 105 ident: CR8 article-title: Analysis on risk factors for cervical cancer using induction technique publication-title: Expert Syst Appl doi: 10.1016/j.eswa.2003.12.005 – volume: 13 start-page: 446 year: 2017 end-page: 457 ident: CR16 article-title: Cervical cancer diagnosis based on random forest publication-title: Int J Performabil Eng – volume: 115 start-page: 557 year: 2019 end-page: 564 ident: CR1 article-title: Classification and diagnosis of cervical cancer with softmax classification with stacked autoencoder publication-title: Expert Syst Appl doi: 10.1016/j.eswa.2018.08.050 – volume: 19 start-page: 3571 issue: 12 year: 2018 ident: CR4 article-title: Automatic approach for cervical cancer detection and segmentation using neural network classifier publication-title: Asian Pac J Cancer Prev: APJCP doi: 10.31557/APJCP.2018.19.12.3571 – ident: CR13 – ident: CR10 – ident: CR11 – volume: 15 start-page: 169 year: 2018 end-page: 182 ident: CR14 article-title: EEG signal classification using universum support vector machine publication-title: Expert Syst Appl doi: 10.1016/j.eswa.2018.03.053 – volume: 5 start-page: 25189 year: 2017 end-page: 25195 ident: CR18 article-title: Data-driven diagnosis of cervical cancer with support vector machine-based approaches publication-title: IEEE Access doi: 10.1109/ACCESS.2017.2763984 – ident: CR9 – volume: 51 start-page: 1357 issue: 12 year: 2013 end-page: 1365 ident: CR12 article-title: Application of gene expression programming and neural networks to predict adverse events of radical hysterectomy in cervical cancer patients publication-title: Med Biol Eng Comput doi: 10.1007/s11517-013-1108-8 – volume: 8 start-page: 65 issue: 2 year: 2015 end-page: 74 ident: CR19 article-title: Prediction using hierarchical data: Applications for automated detection of cervical cancer publication-title: Stat Anal Data Mining: ASA Data Sci J doi: 10.1002/sam.11261 – year: 2012 ident: CR5 publication-title: GLOBOCAN 2012, Cancer Incidence and Mortality Worldwide in 2012 – ident: CR6 – volume: 3 start-page: 25 issue: 1 year: 2000 end-page: 35 ident: CR7 article-title: Abnormal genital tract bleeding publication-title: Clin Cornerstone doi: 10.1016/S1098-3597(00)90019-X – volume: 22 start-page: 962 issue: 6 year: 2011 end-page: 8 ident: CR15 article-title: Improvements on twin support vector machines publication-title: IEEE Trans Neural Netw doi: 10.1109/TNN.2011.2130540 – volume: 10 start-page: 1 issue: 1 year: 2018 end-page: 17 ident: CR3 article-title: Classification of PolSAR images using multilayer autoencoders and a self-paced learning approach publication-title: Remote Sens doi: 10.3390/rs10010110 – volume: 21 start-page: 1633 issue: 6 year: 2017 end-page: 1643 ident: CR20 article-title: Deep convolutional networks for cervical cell classification publication-title: IEEE J Biomed Health Inform doi: 10.1109/JBHI.2017.2705583 – volume: 22 start-page: 39 issue: 1 year: 2017 end-page: 42 ident: CR17 article-title: A study on cervical cancer screening in symptomatic women using Pap smear in a tertiary care hospital in rural area of Himachal Pradesh, India publication-title: Middle East Fertil Soc J doi: 10.1016/j.mefs.2016.09.002 – volume: 21 start-page: 1633 issue: 6 year: 2017 ident: 9607_CR20 publication-title: IEEE J Biomed Health Inform doi: 10.1109/JBHI.2017.2705583 – volume: 15 start-page: 169 year: 2018 ident: 9607_CR14 publication-title: Expert Syst Appl doi: 10.1016/j.eswa.2018.03.053 – volume-title: GLOBOCAN 2012, Cancer Incidence and Mortality Worldwide in 2012 year: 2012 ident: 9607_CR5 – volume: 8 start-page: 65 issue: 2 year: 2015 ident: 9607_CR19 publication-title: Stat Anal Data Mining: ASA Data Sci J doi: 10.1002/sam.11261 – volume: 3 start-page: 25 issue: 1 year: 2000 ident: 9607_CR7 publication-title: Clin Cornerstone doi: 10.1016/S1098-3597(00)90019-X – volume: 22 start-page: 962 issue: 6 year: 2011 ident: 9607_CR15 publication-title: IEEE Trans Neural Netw doi: 10.1109/TNN.2011.2130540 – ident: 9607_CR6 doi: 10.4249/scholarpedia.1717 – volume: 5 start-page: 25189 year: 2017 ident: 9607_CR18 publication-title: IEEE Access doi: 10.1109/ACCESS.2017.2763984 – volume: 22 start-page: 39 issue: 1 year: 2017 ident: 9607_CR17 publication-title: Middle East Fertil Soc J doi: 10.1016/j.mefs.2016.09.002 – volume: 51 start-page: 1357 issue: 12 year: 2013 ident: 9607_CR12 publication-title: Med Biol Eng Comput doi: 10.1007/s11517-013-1108-8 – volume: 13 start-page: 446 year: 2017 ident: 9607_CR16 publication-title: Int J Performabil Eng – volume: 27 start-page: 97 issue: 1 year: 2004 ident: 9607_CR8 publication-title: Expert Syst Appl doi: 10.1016/j.eswa.2003.12.005 – volume: 10 start-page: 1 issue: 1 year: 2018 ident: 9607_CR3 publication-title: Remote Sens doi: 10.3390/rs10010110 – ident: 9607_CR9 – volume: 114 start-page: 281 year: 2017 ident: 9607_CR2 publication-title: Procedia Comput Sci doi: 10.1016/j.procs.2017.09.044 – volume: 19 start-page: 3571 issue: 12 year: 2018 ident: 9607_CR4 publication-title: Asian Pac J Cancer Prev: APJCP doi: 10.31557/APJCP.2018.19.12.3571 – ident: 9607_CR11 – volume: 115 start-page: 557 year: 2019 ident: 9607_CR1 publication-title: Expert Syst Appl doi: 10.1016/j.eswa.2018.08.050 – ident: 9607_CR10 – ident: 9607_CR13 doi: 10.23919/FRUCT.2017.8071332 |
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| SubjectTerms | 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 |
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| Title | DCAVN: Cervical cancer prediction and classification using deep convolutional and variational autoencoder network |
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