IDEAS home Printed from https://ideas.repec.org/a/hin/complx/5531585.html
   My bibliography  Save this article

Modeling of Human Skin by the Use of Deep Learning

Author

Listed:
  • Xin Xiong
  • Xuexun Guo
  • Yiping Wang
  • M. Irfan Uddin

Abstract

Deep learning (DL) has matured well over time and resonated in various domains of computer applications. Pattern recognition gets more attention in machine learning field to take advantage of data available for modern life. Recognition by using the technology performance is worthy in terms of skin and other human features; this research tries to extract useful features from the skin and then classify these features under certain condition. The main objective of this study is to detect the skin diseases early and classify them for correct treatment. Using improved classifier (ISVM) to be adaptive with requirements of our task, many advantages can be got with this technique and it is useful in the fields of medicine, human health care, and diagnosis and life threat. Applying good classifier with best feature selection achieved good result in terms of accuracy, 95%, and recognition rate, 93%. This study concluded that adopting best strategy in selecting features and classification yields better prediction in emergency case before medicating the patient even during treatment.

Suggested Citation

  • Xin Xiong & Xuexun Guo & Yiping Wang & M. Irfan Uddin, 2021. "Modeling of Human Skin by the Use of Deep Learning," Complexity, Hindawi, vol. 2021, pages 1-11, July.
  • Handle: RePEc:hin:complx:5531585
    DOI: 10.1155/2021/5531585
    as

    Download full text from publisher

    File URL: http://downloads.hindawi.com/journals/complexity/2021/5531585.pdf
    Download Restriction: no

    File URL: http://downloads.hindawi.com/journals/complexity/2021/5531585.xml
    Download Restriction: no

    File URL: https://libkey.io/10.1155/2021/5531585?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:hin:complx:5531585. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Mohamed Abdelhakeem (email available below). General contact details of provider: https://www.hindawi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.