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TopoResNet: A Hybrid Deep Learning Architecture and Its Application to Skin Lesion Classification

Author

Listed:
  • Chuan-Shen Hu

    (Department of Mathematics, National Taiwan Normal University, Taipei City 11365, Taiwan)

  • Austin Lawson

    (Department of Mathematics, University of Tennessee Knoxville, Knoxville, TN 37916, USA)

  • Jung-Sheng Chen

    (Department of Medical Research, E-Da Hospital, Kaohsiung City 824410, Taiwan)

  • Yu-Min Chung

    (Eli Lilly and Company, Indianapolis, IN 46225, USA
    The work was done when Y.-M. Chung was employed at University of North Carolina at Greensboro.)

  • Clifford Smyth

    (Department of Mathematics and Statistics, University of North Carolina at Greensboro, Greensboro, NC 27412, USA)

  • Shih-Min Yang

    (Department of Computer Science and Information Engineering, National Taiwan Normal University, Taipei City 11365, Taiwan)

Abstract

The application of artificial intelligence (AI) to various medical subfields has been a popular topic of research in recent years. In particular, deep learning has been widely used and has proven effective in many cases. Topological data analysis (TDA)—a rising field at the intersection of mathematics, statistics, and computer science—offers new insights into data. In this work, we develop a novel deep learning architecture that we call TopoResNet that integrates topological information into the residual neural network architecture. To demonstrate TopoResNet, we apply it to a skin lesion classification problem. We find that TopoResNet improves the accuracy and the stability of the training process.

Suggested Citation

  • Chuan-Shen Hu & Austin Lawson & Jung-Sheng Chen & Yu-Min Chung & Clifford Smyth & Shih-Min Yang, 2021. "TopoResNet: A Hybrid Deep Learning Architecture and Its Application to Skin Lesion Classification," Mathematics, MDPI, vol. 9(22), pages 1-22, November.
    • Handle: RePEc:gam:jmathe:v:9:y:2021:i:22:p:2924-:d:680919
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    References listed on IDEAS

    as
    1. Coccia, Mario, 2020. "Deep learning technology for improving cancer care in society: New directions in cancer imaging driven by artificial intelligence," Technology in Society, Elsevier, vol. 60(C).
    2. C. J. Carstens & K. J. Horadam, 2013. "Persistent Homology of Collaboration Networks," Mathematical Problems in Engineering, Hindawi, vol. 2013, pages 1-7, June.
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    Cited by:

    1. Iftikhar Ahmad & Abdul Qayyum & Brij B. Gupta & Madini O. Alassafi & Rayed A. AlGhamdi, 2022. "Ensemble of 2D Residual Neural Networks Integrated with Atrous Spatial Pyramid Pooling Module for Myocardium Segmentation of Left Ventricle Cardiac MRI," Mathematics, MDPI, vol. 10(4), pages 1-23, February.

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