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ORIGINAL RESEARCH article

Front. Oncol.
Sec. Cancer Imaging and Image-directed Interventions
Volume 14 - 2024 | doi: 10.3389/fonc.2024.1408199

An optimized Support Vector Machine (SVM) for Lung Cancer Classification System

Provisionally accepted
Oluwasegun Julius Aroba Oluwasegun Julius Aroba 1*Mayowa O Oyediran Mayowa O Oyediran 2*Ibrahim A Raji Ibrahim A Raji 2Olufemi S Ojo Olufemi S Ojo 2,3*Abidemi Emmanuel Adeniyi Abidemi Emmanuel Adeniyi 4
  • 1 Durban University of Technology, Durban, South Africa
  • 2 Ajayi Crowther University, Abeokuta, Nigeria
  • 3 Emmanuel Alayande College of Education, Oyo, Nigeria
  • 4 Bowen University, Iwo, Nigeria

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

    Lung cancer is one of the main causes of the rising death rate among the expanding population. For lung cancer patients to have a higher chance of survival and fewer deaths, early categorization is essential. The goal of this research is to enhance machine learning to increase the precision and quality of lung cancer classification. The dataset was obtained from an open-source database and was utilized for testing and training. The suggested system used a CT scan picture as its input image, and it underwent a variety of image processing operations on it, including segmentation, contrast enhancement, and feature extraction. The training process produces a chameleon Swarm Based Support Vector Machine that can identify between benign, malignant, and normal nodules. The performance of the system is evaluated in terms of false positive rate, sensitivity, specificity accuracy and recognition time.

    Keywords: Chameleon Swarm Algorithm (CSA), lung cancer, Support vector machine, optimization techniques, machine learning

    Received: 27 Mar 2024; Accepted: 11 Sep 2024.

    Copyright: © 2024 Aroba, Oyediran, Raji, Ojo and Adeniyi. 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:
    Oluwasegun Julius Aroba, Durban University of Technology, Durban, South Africa
    Mayowa O Oyediran, Ajayi Crowther University, Abeokuta, Nigeria
    Olufemi S Ojo, Ajayi Crowther University, Abeokuta, Nigeria

    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.