Classification of tissue types in histological colorectal cancer images using residual networks

Ko Hua Lai, Meng Chou Chang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Colorectal cancer (CRC) is one of the most prevalent cancer types, but until now only few studies have addressed the problem of automatic recognition of distinct tissue types in histological CRC images. In this paper, we employ two deep convolutional neural networks, the original 50-layer residual network ResNet-50 and a modified residual network ResNet-50-fla-drop, to automatically perform eight-class tissue separation in histological CRC images. Experimental results showed that the proposed modified residual network ResNet-50-fla-drop can achieve a classification accuracy of 94.4% for eight-class CRC tissue separation, and it outperforms other published machine learning methods, including the radial-basis function SVM and VGG-VD-16.

Original languageEnglish
Title of host publication2019 IEEE 8th Global Conference on Consumer Electronics, GCCE 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages299-300
Number of pages2
ISBN (Electronic)9781728135755
DOIs
Publication statusPublished - 2019 Oct
Event8th IEEE Global Conference on Consumer Electronics, GCCE 2019 - Osaka, Japan
Duration: 2019 Oct 152019 Oct 18

Publication series

Name2019 IEEE 8th Global Conference on Consumer Electronics, GCCE 2019

Conference

Conference8th IEEE Global Conference on Consumer Electronics, GCCE 2019
CountryJapan
CityOsaka
Period19-10-1519-10-18

All Science Journal Classification (ASJC) codes

  • Instrumentation
  • Artificial Intelligence
  • Computer Networks and Communications
  • Computer Science Applications
  • Electrical and Electronic Engineering

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  • Cite this

    Lai, K. H., & Chang, M. C. (2019). Classification of tissue types in histological colorectal cancer images using residual networks. In 2019 IEEE 8th Global Conference on Consumer Electronics, GCCE 2019 (pp. 299-300). [9015250] (2019 IEEE 8th Global Conference on Consumer Electronics, GCCE 2019). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/GCCE46687.2019.9015250