An unconventional SVM classification using Chaos Pso optimization for lung cancer discovery
| Autor: | S Sanjith, C Thinkal Dayana, K S Mithra |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
Multidisciplinary
Computer science business.industry Quantitative Biology::Tissues and Organs Particle swarm optimization Feature selection Pattern recognition Support vector machine CHAOS (operating system) Svm classifier ComputingMethodologies_PATTERNRECOGNITION Classifier (linguistics) Genetic algorithm Artificial intelligence business MATLAB computer computer.programming_language |
| Zdroj: | Indian Journal of Science and Technology. 14:527-533 |
| ISSN: | 0974-5645 0974-6846 |
| Popis: | 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 |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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