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Lung Disease Classification by Novel Shape-Based Feature Extraction and New Hybrid Genetic Approach: Lung Disease Classification by Shape-Based Method

Lung Disease Classification by Novel Shape-Based Feature Extraction and New Hybrid Genetic Approach: Lung Disease Classification by Shape-Based Method

Bhuvaneswari Chandran, P. Aruna, D. Loganathan
Copyright: © 2017 |Pages: 26
ISBN13: 9781522505716|ISBN10: 1522505717|EISBN13: 9781522505723
DOI: 10.4018/978-1-5225-0571-6.ch077
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MLA

Chandran, Bhuvaneswari, et al. "Lung Disease Classification by Novel Shape-Based Feature Extraction and New Hybrid Genetic Approach: Lung Disease Classification by Shape-Based Method." Medical Imaging: Concepts, Methodologies, Tools, and Applications, edited by Information Resources Management Association, IGI Global, 2017, pp. 1885-1910. https://doi.org/10.4018/978-1-5225-0571-6.ch077

APA

Chandran, B., Aruna, P., & Loganathan, D. (2017). Lung Disease Classification by Novel Shape-Based Feature Extraction and New Hybrid Genetic Approach: Lung Disease Classification by Shape-Based Method. In I. Management Association (Ed.), Medical Imaging: Concepts, Methodologies, Tools, and Applications (pp. 1885-1910). IGI Global. https://doi.org/10.4018/978-1-5225-0571-6.ch077

Chicago

Chandran, Bhuvaneswari, P. Aruna, and D. Loganathan. "Lung Disease Classification by Novel Shape-Based Feature Extraction and New Hybrid Genetic Approach: Lung Disease Classification by Shape-Based Method." In Medical Imaging: Concepts, Methodologies, Tools, and Applications, edited by Information Resources Management Association, 1885-1910. Hershey, PA: IGI Global, 2017. https://doi.org/10.4018/978-1-5225-0571-6.ch077

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Abstract

The purpose of the chapter is to present a novel method to classify lung diseases from the computed tomography images which assist physicians in the diagnosis of lung diseases. The method is based on a new approach which combines a proposed M2 feature extraction method and a novel hybrid genetic approach with different types of classifiers. The feature extraction methods performed in this work are moment invariants, proposed multiscale filter method and proposed M2 feature extraction method. The essential features which are the results of the feature extraction technique are selected by the novel hybrid genetic algorithm feature selection algorithms. Classification is performed by the support vector machine, multilayer perceptron neural network and Bayes Net classifiers. The result obtained proves that the proposed technique is an efficient and robust method. The performance of the proposed M2 feature extraction with proposed hybrid GA and SVM classifier combination achieves maximum classification accuracy.

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