Skip to main content
placeholder image

HEp-2 cell image classification with convolutional neural networks

Conference Paper


Abstract


  • The diagnosis of many autoimmune diseases can be greatly facilitated by automatic staining patterns classification of Human Epithelial-2 (HEp-2) cells within indirect immunofluorescence (IIF) images. In this paper, we propose a framework to classify the HEp-2 cells by utilizing the deep convolutional neural networks (CNNs). With carefully designed network architecture and optimized parameters, our networks extract features from raw pixels of cell images in a hierarchical manner and perform classification jointly, avoiding using hand-crafted features to represent a HEp-2 cell image. We evaluate our method on the training dataset of HEp-2 cells classification competition held by ICPR 2014. Our system achieves mean class accuracy of 96.7% on the held-out test set and it also obtains competitive performance on the ICPR 2012 cell dataset.

Publication Date


  • 2014

Citation


  • Gao, Z., Zhang, J., Zhou, L., & Wang, L. (2014). HEp-2 cell image classification with convolutional neural networks. In Proceedings - 2014 1st Workshop on Pattern Recognition Techniques for Indirect Immunofluorescence Images, I3A Workshop 2014 (pp. 24-28). doi:10.1109/I3A.2014.15

Scopus Eid


  • 2-s2.0-84919414531

Web Of Science Accession Number


Start Page


  • 24

End Page


  • 28

Abstract


  • The diagnosis of many autoimmune diseases can be greatly facilitated by automatic staining patterns classification of Human Epithelial-2 (HEp-2) cells within indirect immunofluorescence (IIF) images. In this paper, we propose a framework to classify the HEp-2 cells by utilizing the deep convolutional neural networks (CNNs). With carefully designed network architecture and optimized parameters, our networks extract features from raw pixels of cell images in a hierarchical manner and perform classification jointly, avoiding using hand-crafted features to represent a HEp-2 cell image. We evaluate our method on the training dataset of HEp-2 cells classification competition held by ICPR 2014. Our system achieves mean class accuracy of 96.7% on the held-out test set and it also obtains competitive performance on the ICPR 2012 cell dataset.

Publication Date


  • 2014

Citation


  • Gao, Z., Zhang, J., Zhou, L., & Wang, L. (2014). HEp-2 cell image classification with convolutional neural networks. In Proceedings - 2014 1st Workshop on Pattern Recognition Techniques for Indirect Immunofluorescence Images, I3A Workshop 2014 (pp. 24-28). doi:10.1109/I3A.2014.15

Scopus Eid


  • 2-s2.0-84919414531

Web Of Science Accession Number


Start Page


  • 24

End Page


  • 28