21 July 2017 HEp-2 cell image classification method based on very deep convolutional networks with small datasets
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Proceedings Volume 10420, Ninth International Conference on Digital Image Processing (ICDIP 2017); 1042040 (2017) https://doi.org/10.1117/12.2282033
Event: Ninth International Conference on Digital Image Processing (ICDIP 2017), 2017, Hong Kong, China
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.
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Mengchi Lu, Mengchi Lu, Long Gao, Long Gao, Xifeng Guo, Xifeng Guo, Qiang Liu, Qiang Liu, Jianping Yin, Jianping Yin, } "HEp-2 cell image classification method based on very deep convolutional networks with small datasets", Proc. SPIE 10420, Ninth International Conference on Digital Image Processing (ICDIP 2017), 1042040 (21 July 2017); doi: 10.1117/12.2282033; https://doi.org/10.1117/12.2282033
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