The Utilization of Padding Scheme on Convolutional Neural Network for Cervical Cell Images Classification

Toto Haryanto, Imas Sukaesih Sitanggang, Muhammad Ashyar Agmalaro, Riries Rulaningtyas

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

22 Citations (Scopus)

Abstract

Cervical cancer identification through pap-smear images analysis is a challenge for medicians, especially in distinguishing cells, between the normal and abnormal one. This study aims to create the classification model of Cervical Cell Images using the Convolutional Neural Network (CNN) algorithm. The dataset used is the image dataset SIPaKMeD. The CNN algorithm was implemented using the AlexNet architecture with and non-padding scheme. Padding is included in the experiments by adding the pixel 0 on the original images to improve the accuracy of the model. The experimental results show that using the utilization padding scheme on the AlexNet architecture can increase the accuracy of the model slightly significantly from 84.88% to 87.32%.

Original languageEnglish
Title of host publicationCENIM 2020 - Proceeding
Subtitle of host publicationInternational Conference on Computer Engineering, Network, and Intelligent Multimedia 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages34-38
Number of pages5
ISBN (Electronic)9781728182834
DOIs
Publication statusPublished - 17 Nov 2020
Event2020 International Conference on Computer Engineering, Network, and Intelligent Multimedia, CENIM 2020 - Virtual, Surabaya, Indonesia
Duration: 17 Nov 202018 Nov 2020

Publication series

NameCENIM 2020 - Proceeding: International Conference on Computer Engineering, Network, and Intelligent Multimedia 2020

Conference

Conference2020 International Conference on Computer Engineering, Network, and Intelligent Multimedia, CENIM 2020
Country/TerritoryIndonesia
CityVirtual, Surabaya
Period17/11/2018/11/20

Keywords

  • AlexNet
  • Cervical cancer
  • Convolutional Neural Network
  • SIPaKMeD

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