Deep convolutional neural network based HEp-2 cell classification

Xi Jia, Linlin Shen, Xiande Zhou, Shiqi Yu

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

26 Citations (Scopus)

Abstract

As different staining patterns of HEp-2 cells indicate different diseases, the classification of Indirect Immune Fluorescence (IIF) images on Human Epithelial-2 (HEp-2) cell is important for clinical applications. Different from traditional pattern recognition techniques, we use CNN to extract more high-level features for cell images classification. Compared to the existing CNN based HEp-2 classification methods, we proposed a network with deeper architecture. A class-balanced approach is also proposed to augment the HEp-2 cell dataset for network training. The proposed framework achieves an average class accuracy of 79.29% on ICPR 2012 HEp-2 dataset and a mean class accuracy of 98.26% on ICPR 2016 HEp-2 training set.

Original languageEnglish
Title of host publication2016 23rd International Conference on Pattern Recognition, ICPR 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages77-80
Number of pages4
ISBN (Electronic)9781509048472
DOIs
Publication statusPublished - 1 Jan 2016
Externally publishedYes
Event23rd International Conference on Pattern Recognition, ICPR 2016 - Cancun, Mexico
Duration: 4 Dec 20168 Dec 2016

Publication series

NameProceedings - International Conference on Pattern Recognition
Volume0
ISSN (Print)1051-4651

Conference

Conference23rd International Conference on Pattern Recognition, ICPR 2016
Country/TerritoryMexico
CityCancun
Period4/12/168/12/16

Keywords

  • Class-balanced
  • Classification
  • CNN
  • Hep-2

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition

Fingerprint

Dive into the research topics of 'Deep convolutional neural network based HEp-2 cell classification'. Together they form a unique fingerprint.

Cite this