• P-ISSN 0974-6846 E-ISSN 0974-5645

Indian Journal of Science and Technology


Indian Journal of Science and Technology

Year: 2021, Volume: 14, Issue: 6, Pages: 527-533

Original Article

An unconventional SVM classification using Chaos Pso optimization for lung cancer discovery

Received Date:09 October 2021, Accepted Date:10 February 2021, Published Date:22 February 2021


Objectives: The main purpose of this work is to detect the cancer region and to classify the particular region based on Support Vector Machine (SVM) classifier. Methods: Optimization technique is used after classifying the cancerous region in order to improve the accuracy of the Lung cancer CT images. The proposed method is improved using a novel Chaos Particle Swarm Optimization (CPSO) technique. The MATLAB is used to optimize the technique. Findings: The achieved accuracy of SVM classifier using CPSO is 97.4% which is higher when compared to PSO, Genetic algorithm which yields an accuracy 89.5% and genetic optimization for feature selection and ANN for lung cancer classification which obtains 95.87% accuracy.

Keywords: Chaos Particle Swarm Optimization; SVM; CT image; classification


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© 2021 Thinkal Dayana et al.This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Published By Indian Society for Education and Environment (iSee)


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