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HEp-2 image classification using a multi-class and multiple-binary classifier

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Abstract

In medicine, identifying the indirect immunofluorescence of human epithelial type 2 (HEp-2) cells plays a decisive role in the diagnosis of autoimmune diseases. The manual interpretation of Hep-2 cell images may lead to some limitations, such as subjectivity, inconsistency and low efficiency. Therefore, it is very important to automatically identify HEp-2 images. Inspired by the outstanding performance of neural networks in image classification tasks, we propose a multi-class and multiple-binary classifier (MCMBC) for the classification of HEp-2 cells. MCMBC is an ensemble learner that contains two kinds of sub-classifiers: multi-class (MC) and multiple-binary (MB). The MC sub-classifier adopts a multi-scale convolutional neural network (MSCNN) that increases the efficiency of information transmission between layers. On the basis of classification results of the MC sub-classifier on validation sets, we can find easy-to-confuse class pairs. An easy-to-confuse class pair is two classes that are not easy to be identified from each other. The MB sub-classifiers adopt multiple-binary pre-trained VGG16 networks that are used to deal with these class pairs. The final prediction for a sample possibly belonging to an easy-to-confuse class is decided by the assembled features extracted from the last fully connected layer of MC and the output of MB sub-classifiers. To evaluate the proposed model, experiments were conducted on the ICPR 2014 Task-2 dataset. Experimental results show that MCMBC performs better than the state-of-the-art method (84.68% vs. 83.35% on the criterion of average classification accuracy (ACA) and 82.89% vs. 82.67% on the criterion of mean classification accuracy (MCA)).

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Acknowledgements

We would like to thank anonymous reviewers and Editor Arvind Pathak for their valuable comments and suggestions, which have significantly improved this paper.

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Authors and Affiliations

  1. School of Computer Science and Technology, Soochow University, Suzhou, 215006, Jiangsu, China

    Li Zhang, Meng-Qian Zhang & Xuerui Lv

  2. Provincial Key Laboratory for Computer Information Processing Technology, Soochow University, Suzhou, 215006, Jiangsu, China

    Li Zhang

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  1. Li Zhang
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Correspondence to Li Zhang.

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Supported by the Natural Science Foundation of the Jiangsu Higher Education Institutions of China under Grant No. 19KJA550002, the Six Talent Peak Project of Jiangsu Province of China under Grant No. XYDXX-054, and the Priority Academic Program Development of Jiangsu Higher Education Institutions.

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Zhang, L., Zhang, MQ. & Lv, X. HEp-2 image classification using a multi-class and multiple-binary classifier. Med Biol Eng Comput 60, 3113–3124 (2022). https://doi.org/10.1007/s11517-022-02646-5

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