Abstract
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
Highlights
Lung cancer is the most common disorders which affects the people and can reduce the survival rate
The initial identification is not an easy task which requires some fundamentals image processing steps followed by Optimization technique [1]
In the dataset 75% data is used for training and 25% used for testing process
Summary
Lung cancer is the most common disorders which affects the people and can reduce the survival rate. In [3] proposed the modified bacterial foraging optimization technique for lung cancer classification which evaluates the performance parameter It shows the simulated result for back propagation neural network is better than SVM method. MLP-NN lung disease classification technique using PSO algorithm which improves the feature selection and analyzed the accuracy, bit error rate[4,5,6,7,8]. A new idea for identifying the lung cancer status which uses genetic optimization for feature selection and ANN for lung cancer classification.
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