Skip to main content

ORIGINAL RESEARCH article

Front. Big Data
Sec. Medicine and Public Health
Volume 7 - 2024 | doi: 10.3389/fdata.2024.1506567

A Supervised Learning-Based Classification Technique for Precise Identification of Monkeypox Using Skin Imaging

Provisionally accepted
Vandana - Vandana - 1,2Chetna Sharma Chetna Sharma 1*Yonis Gulzar Yonis Gulzar 3*Mohammad Shuaib Mir Mohammad Shuaib Mir 3
  • 1 Chitkara University, Chandigarh, Punjab, India
  • 2 Mukand Lal National College, Haryana, India
  • 3 King Faisal University, Al-Ahsa, Saudi Arabia

The final, formatted version of the article will be published soon.

    The monkeypox epidemic has spread to nearly every nation. For effective handling and treatment, early identification and diagnosis of monkeypox using digital skin lesion images is critical. This article presents a supervised learning-based classification method designed for the precise identification of monkeypox cases using deep learning. The analysis was conducted using an opensource dataset from Kaggle, consisting of digital images of monkeypox, which were processed using advanced image processing and deep learning techniques. The data was categorized based on findings related and unrelated to monkeypox. Resnet with 50 layers and up to 35 folds was utilized to identify regions of interest, which could be indicative of characteristics relevant to computer-assisted medical diagnosis and enable us to solve image processing and natural language processing tasks with high accuracy. In terms of performance, the accuracy of 96% and precision is 95% achieved during cross-validation classification testing. This outcome demonstrates the potential for computer-assisted diagnosis as a supplementary tool for medical professionals. Amid the monkeypox outbreak, this method offers a technical and objective assessment of patients' skin conditions, thereby simplifying the diagnostic process for specialists.

    Keywords: deep learning, Monkeypox, Medical Image Processing, image classification, cross validation

    Received: 07 Oct 2024; Accepted: 08 Nov 2024.

    Copyright: © 2024 -, Sharma, Gulzar and Mir. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence:
    Chetna Sharma, Chitkara University, Chandigarh, 160 009, Punjab, India
    Yonis Gulzar, King Faisal University, Al-Ahsa, Saudi Arabia

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.