Convolutional Neural Network (CNN) for gland images classification

Toto Haryanto, Ito Wasito, Heru Suhartanto

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

4 Citations (Scopus)

Abstract

An automatic detection of histopathological images has an important role in helping diagnose step. Even, for determining the status of cancer, benign or malignant A conventional way in cancer detection has infirmity like user dependency, the tendency to the incorrect identification and takes more time. Convolutional Neural Network (CNN) is one of the deep learning architecture that can accommodate automatic feature extraction and classification directly. The ability of CNN to extract a feature of an image in depth underlie our research. The research aims to classify the two statuses of cancer on gland images using CNN. The training process for six, eight and ten layers exploited on this research. The accuracy obtained up to 82.98, 81.91 and 89.36 percent for six, eight and ten layers respectively. But in the future, we need to improve the computing time.

Original languageEnglish
Title of host publicationProceedings of the 11th International Conference on Information and Communication Technology and System, ICTS 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages55-59
Number of pages5
ISBN (Electronic)9781538628256
DOIs
Publication statusPublished - 19 Jan 2018
Event11th International Conference on Information and Communication Technology and System, ICTS 2017 - Surabaya, Indonesia
Duration: 31 Oct 201731 Oct 2017

Publication series

NameProceedings of the 11th International Conference on Information and Communication Technology and System, ICTS 2017
Volume2018-January

Conference

Conference11th International Conference on Information and Communication Technology and System, ICTS 2017
Country/TerritoryIndonesia
CitySurabaya
Period31/10/1731/10/17

Keywords

  • CNN
  • deep learning
  • gland images

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