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ORIGINAL RESEARCH article

Front. Med.
Sec. Pathology
Volume 11 - 2024 | doi: 10.3389/fmed.2024.1487270
This article is part of the Research Topic Artificial Intelligence-Assisted Medical Imaging Solutions for Integrating Pathology and Radiology Automated Systems - Volume II View all 3 articles

LesionNet: An Automated Approach For Skin Lesion Classification Using SIFT Features With Customized Convolutional Neural Network

Provisionally accepted
Muhammad Umer Muhammad Umer 1Sarah A. Alzakari Sarah A. Alzakari 2Stephen Ojo Stephen Ojo 3*James Wanliss James Wanliss 3Shtwai Alsubai Shtwai Alsubai 4Areej Alasiry Areej Alasiry 5Mehrez Marzougui Mehrez Marzougui 5Nisreen Innab Nisreen Innab 6*
  • 1 Islamia University of Bahawalpur, Bahawalpur, Pakistan
  • 2 Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia
  • 3 Anderson University, Anderson, Indiana, United States
  • 4 Department of Computer Science, College of Computer Engineering and Sciences, Prince Sattam Bin Abdulaziz University, Alkharj, Saudi Arabia
  • 5 College of Computer Science, King Khalid University, Abha, Saudi Arabia
  • 6 Department of Computer Science and Information Systems, College of Applied Sciences, University of Almaarefa, Dariyah, Riyadh, Saudi Arabia

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

    Accurate detection of skin lesions through computer-aided diagnosis has emerged as a critical advancement in dermatology, addressing the inefficiencies and errors inherent in manual visual analysis. Despite the promise of automated diagnostic approaches, challenges such as image size variability, hair artifacts, color inconsistencies, ruler markers, low contrast, lesion dimension differences, and gel bubbles must be overcome. Researchers have made significant strides in binary classification problems, particularly in distinguishing melanocytic lesions from normal skin conditions. Leveraging the "MNIST HAM10000" dataset from the International Skin Image Collaboration, this study integrates Scale-Invariant Feature Transform (SIFT) features with a custom convolutional neural network model called LesionNet. The experimental results reveal the model's robustness, achieving an impressive accuracy of 99.28%. This high accuracy underscores the effectiveness of combining feature extraction techniques with advanced neural network models in enhancing the precision of skin lesion detection.

    Keywords: Skin lesion classification, Computer Vision, Customized CNN, SIFT features, deep learning

    Received: 27 Aug 2024; Accepted: 02 Oct 2024.

    Copyright: © 2024 Umer, Alzakari, Ojo, Wanliss, Alsubai, Alasiry, Marzougui and Innab. 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:
    Stephen Ojo, Anderson University, Anderson, Indiana, United States
    Nisreen Innab, Department of Computer Science and Information Systems, College of Applied Sciences, University of Almaarefa, Dariyah, 71666, Riyadh, 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.