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Proceedings Paper

HEp-2 cell image classification method based on very deep convolutional networks with small datasets
Author(s): Mengchi Lu; Long Gao; Xifeng Guo; Qiang Liu; Jianping Yin
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Paper Abstract

Human Epithelial-2 (HEp-2) cell images staining patterns classification have been widely used to identify autoimmune diseases by the anti-Nuclear antibodies (ANA) test in the Indirect Immunofluorescence (IIF) protocol. Because manual test is time consuming, subjective and labor intensive, image-based Computer Aided Diagnosis (CAD) systems for HEp-2 cell classification are developing. However, methods proposed recently are mostly manual features extraction with low accuracy. Besides, the scale of available benchmark datasets is small, which does not exactly suitable for using deep learning methods. This issue will influence the accuracy of cell classification directly even after data augmentation. To address these issues, this paper presents a high accuracy automatic HEp-2 cell classification method with small datasets, by utilizing very deep convolutional networks (VGGNet). Specifically, the proposed method consists of three main phases, namely image preprocessing, feature extraction and classification. Moreover, an improved VGGNet is presented to address the challenges of small-scale datasets. Experimental results over two benchmark datasets demonstrate that the proposed method achieves superior performance in terms of accuracy compared with existing methods.

Paper Details

Date Published: 21 July 2017
PDF: 6 pages
Proc. SPIE 10420, Ninth International Conference on Digital Image Processing (ICDIP 2017), 1042040 (21 July 2017); doi: 10.1117/12.2282033
Show Author Affiliations
Mengchi Lu, National Univ. of Defense Technology (China)
Long Gao, National Univ. of Defense Technology (China)
Xifeng Guo, National Univ. of Defense Technology (China)
Qiang Liu, National Univ. of Defense Technology (China)
Jianping Yin, National Univ. of Defense Technology (China)


Published in SPIE Proceedings Vol. 10420:
Ninth International Conference on Digital Image Processing (ICDIP 2017)
Charles M. Falco; Xudong Jiang, Editor(s)

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