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Classification and Prediction of Erythemato-Squamous Diseases Through Tensor-Based Learning

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Proceedings of the National Academy of Sciences, India Section A: Physical Sciences Aims and scope Submit manuscript

Abstract

The paper proposes a classification algorithm based on support tensor machines which finds the maximum margin between the tensor spaces. The proposed algorithm has been deployed to classify erythemato-squamous diseases (ESDs) with the help of its features. Features are derived from the skin lesion images of ESDs, and it has been represented as second-order tensors, i.e., \( \varvec{X} \in \varvec{ }{\mathbb{R}}^{\varvec{n}} \) can be transformed into \( \varvec{X} \in \,\varvec{ }{\mathbf{\Re }}^{{\varvec{n}_{1} }} \,\varvec{ } \otimes \,{\mathbf{\Re }}^{{\varvec{n}_{2} }} \) where \( n_{1} \times n_{2} \cong n \). After deriving the features from the skin lesion images, dominant features are extracted using Tucker tensor decomposition method. Most of the existing machine learning algorithms depend on the vector-based learning models, and these algorithms suffer from the data overfitting problem. To resolve this problem, in this paper, tensor-based learning is implemented for classification. Proposed algorithm is evaluated with the real-time dataset (Xie et al. in: He, Liu, Krupinski, Xu (eds) Health information science, Springer, Berlin, 2012), and higher classification accuracy of 99.93–100% is achieved. The acquired results are compared with the existing machine learning algorithms, and it drives home the point that the proposed algorithm provides higher classification accuracy.

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Acknowledgements

We the authors would like to thank the editor and the reviewers who have given valuable suggestions to improve the quality of this article. We also thank the Department of Science and Technology, India, for their financial support through Fund for Improvement of S&T Infrastructure (FIST) programme (SR/FST/ETI-349/2013). Our earnest thanks to the SASTRA University for providing all the facilities to proceed with the research work.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Lords Institute of Engineering & Technology, Hyderabad, Telangana, 500 008, India

    N. Badrinath

  2. School of Computing, SASTRA University, Tirumalaisamudram, Thanjavur, Tamilnadu, 613401, India

    G. Gopinath

  3. Computer Vision & Machine Learning Laboratory, School of Computing, SASTRA Deemed University, Thanjavur, 613 401, India

    K. S. Ravichandran, J. Premaladha & R. Krishankumar

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  1. N. Badrinath
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  2. G. Gopinath
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  3. K. S. Ravichandran
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  4. J. Premaladha
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  5. R. Krishankumar
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Correspondence to K. S. Ravichandran.

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Badrinath, N., Gopinath, G., Ravichandran, K.S. et al. Classification and Prediction of Erythemato-Squamous Diseases Through Tensor-Based Learning. Proc. Natl. Acad. Sci., India, Sect. A Phys. Sci. 90, 327–335 (2020). https://doi.org/10.1007/s40010-018-0563-x

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  • DOI: https://doi.org/10.1007/s40010-018-0563-x

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