Detection and Classification of Skin Cancer by using CNN-enabled Cloud Storage Data Access Control Algorithm based on Blockchain Technology

Authors

  • Sana Nasir Department of Computer Engineering, University of HITEC, Taxila, Pakistan
  • Muhammad Bilal Department of Information Technology Rawalpindi Women University, Pakistan https://orcid.org/0000-0002-3823-3905
  • Humaira Khalidi College of Medicine, Alfaisal University, Riyadh, Saudi Arabia

Keywords:

Convolutional Neural Networks, Image Processing, Skin Cancer, Health Risks, Preprocessing

Abstract

Skin cancer is one of the most serious problems in the world. In the circumstance of manual examination by a clinic, the human eye is unable to detect disorders. In this research paper I will discuss the deep learning techniques that help to solve the problem of skin cancer. Skin cancer disease is similar to optical properties that choose the useful feature of images. It’s accurate detection and classification of skin disease through images using CNN (Convolutional Neural Network). We used popular datasets to evaluate the performance of our proposed model. Specifically, the model achieves an accuracy of 99.3% on the Skin Cancer dataset. The aim of this paper is first detection and classification of skin cancer. Secondly, apply the preprocessing technique of skin cancer dataset to find the accuracy of skin cancer disease.

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Published

2025-09-20

How to Cite

Nasir, S., Bilal, M., & Khalidi, H. (2025). Detection and Classification of Skin Cancer by using CNN-enabled Cloud Storage Data Access Control Algorithm based on Blockchain Technology. International Journal of Theoretical & Applied Computational Intelligence, 145–169. Retrieved from https://ijtaci.com/index.php/ojs/article/view/31

Issue

Volume 2025

Section

Articles

License

Copyright (c) 2025 Sana Nasir, Muhammad Bilal, Humaira Khalidi

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Copyright © 2025 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).