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An optimized support vector machine for lung cancer classification system.

Mayowa O Oyediran1, Olufemi S Ojo2, Ibrahim A Raji3

  • 1Department of Computer Engineering, Ajayi Crowther University, Oyo, Nigeria.

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|January 7, 2025
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This study enhances machine learning for precise lung cancer classification using CT scans. A novel support vector machine approach improves early detection, potentially increasing survival rates.

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chameleon swarm algorithm (CSA)lung cancermachine learningoptimization techniquessupport vector machine

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Oncology

Background:

  • Lung cancer is a leading cause of mortality globally.
  • Early and accurate classification of lung nodules is crucial for patient survival.
  • Existing methods require enhancement for improved precision.

Purpose of the Study:

  • To improve the precision and quality of lung cancer classification using machine learning.
  • To develop an enhanced machine learning model for identifying benign, malignant, and normal lung nodules.
  • To contribute to earlier and more accurate diagnosis of lung cancer.

Main Methods:

  • Utilized an open-source dataset of CT scan images for training and testing.
  • Implemented image processing techniques including segmentation and contrast enhancement.
  • Developed a novel system employing a chameleon swarm-based support vector machine (SVM).

Main Results:

  • The chameleon swarm-based SVM effectively distinguishes between benign, malignant, and normal nodules.
  • The system demonstrated high accuracy in lung nodule classification.
  • Performance metrics such as sensitivity, specificity, and accuracy were evaluated.

Conclusions:

  • The proposed machine learning approach enhances lung cancer classification accuracy.
  • Early detection through improved classification can lead to better patient outcomes.
  • The study highlights the potential of advanced AI in oncological diagnostics.