Skin Disease prediction and classification from dermoscopy images using Neural Network

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This is an unedited manuscript accepted for publication and provided as an Article in Press for early access at the author’s request. The article will undergo copyediting, typesetting, and galley proof review before final publication. Please be aware that errors may be identified during production that could affect the content. All legal disclaimers of the journal apply.

Year : 2026 | Volume : 17 | 01 | Page :
    By

    Prasanna Kumar Mishra,

  • Lalit Kumar Behera,

  • Jitendra Kumar Mishra,

  1. Assistant Professor, Department of CSE, Gandhi Institute of Excellent Technocrats, Ghangapatna, Bhubaneswar, Odisha, India
  2. Student, Department of CSE, Gandhi Institute of Excellent Technocrats, Ghangapatna, Bhubaneswar, Odisha, India
  3. Student, Department of CSE, Gandhi Institute of Excellent Technocrats, Ghangapatna, Bhubaneswar, Odisha, India

Abstract

Skin diseases are among the most common health-related problems affecting people of all age groups, and their occurrence often varies with seasonal and environmental conditions. Delayed or incorrect diagnosis of skin disorders can lead to severe complications, making early and accurate detection extremely important for effective treatment and prevention. In recent years, rapid advancements in deep learning and neural network technologies have significantly contributed to the development of automated medical image analysis systems with improved accuracy and reliability. This study presents a comparative analysis of neural network–based approaches for the prediction and classification of five common skin diseases, namely Herpes, Melanoma, Sarampion, Monkeypox, and Chickenpox. The proposed system employs deep neural network models to automatically extract meaningful features from skin lesion image datasets and classify them into predefined disease categories. Image preprocessing and feature learning are performed to enhance classification performance and reduce noise. Multiple neural network architectures are evaluated to analyze their effectiveness in terms of accuracy, precision, and prediction capability. Experimental results demonstrate that the developed models achieve high classification accuracy and show strong potential for reliable disease prediction. The findings indicate that neural network–based automated systems can support dermatologists by providing fast, accurate, and consistent diagnostic assistance, thereby improving early detection and reducing manual diagnostic errors. This approach highlights the growing role of artificial intelligence in medical decision support systems.

Keywords: Skin Disease, Neural Network, CNN, Medical Imaging, Deep Learning, CNN, dermoscopy

How to cite this article:
Prasanna Kumar Mishra, Lalit Kumar Behera, Jitendra Kumar Mishra. Skin Disease prediction and classification from dermoscopy images using Neural Network. Journal of Computer Technology & Applications. 2026; 17(01):-.
How to cite this URL:
Prasanna Kumar Mishra, Lalit Kumar Behera, Jitendra Kumar Mishra. Skin Disease prediction and classification from dermoscopy images using Neural Network. Journal of Computer Technology & Applications. 2026; 17(01):-. Available from: https://journals.stmjournals.com/jocta/article=2026/view=237236


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Ahead of Print Subscription Review Article
Volume 17
01
Received 20/01/2026
Accepted 27/01/2026
Published 20/02/2026
Publication Time 31 Days
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