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

C Thinkal Dayana; K S Mithra; S Sanjith

doi:10.17485/IJST/v14i6.1810

Article

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

VIEWS 1346
PDF 260

Indian Journal of Science and Technology

DOI: 10.17485/IJST/v14i6.1810

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

Original Article

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

C Thinkal Dayana¹, K S Mithra¹, S Sanjith^2*

¹Assistant Professor, Department of Computer Science, St Alphonsa College of Arts and
Science, Karinkal, 629157, India
²Assistant Professor & Head, Department of Computer Science, St Alphonsa College of Arts
and Science, Karinkal, 629157, India. Tel.: +91-9751995269

*Corresponding Author
Tel: +91-9751995269
Email: [email protected]

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

This work is licensed under a Creative Commons Attribution 4.0 International License.

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

References

Arul C, Jaison B, AK. A discrete wavelet based feature extraction and hybrid classification technique for Microarray data analysis. TSWJ. 2014. Available from: https://www.hindawi.com/journals/tswj/2014/195470
Pinheiro CAdP, Nedjah N, Mourelle LdM. Detection and classification of pulmonary nodules using deep learning and swarm intelligence. Multimedia Tools and Applications. 2020;79:15437–15465. Available from: https://dx.doi.org/10.1007/s11042-019-7473-z
Mithra KS, Emmanuel WRS. Automatic Methods for Mycobacterium Detection on Stained Sputum Smear Images: a Survey. Pattern Recognition and Image Analysis. 2018;28(2):310–320. Available from: https://dx.doi.org/10.1134/s105466181802013x
Babita R, Kumar A, Ravneet G, K. A Modified approach for Lung cancer detection using Bacterial Foraging Optimization Algorithm”. International journal of scientific research Engineering& Technology. 2016;5:39–42. Available from: https://www.scribd.com/document/322070115
Tajinder K, Neelakshi G. Classification of Lung Diseases using Particle Swarm Optimization”. International journal of advanced research in Electronics and Communication Engineering. 2015;4:2440–2446. Available from: http://ijarece.org/?page_id=1829
ASAH, Bin-Ghodel ASH. A Particle Swarm based Approach for Classification of Cancer based on CT Scan. International Journal of Computer Applications. 2019;178(12):26–31. Available from: https://doi.org/10.5120/ijca2019918862
PK, S P. A Novel Hybrid Segmentation Method with Particle Swarm Optimization and Fuzzy C-Mean Based On Partitioning the Image for Detecting Lung Cancer. Preprints . 2019. Available from: https://doi.org/10.20944/preprints201906.0195.v1
Rostami M, Forouzandeh S, Berahmand K, Soltani M. Integration of multi-objective PSO based feature selection and node centrality for medical datasets. Genomics. 2020;112(6):4370–4384. Available from: https://dx.doi.org/10.1016/j.ygeno.2020.07.027
Asuntha A, Neelima S, AS. PSO, Genetic optimization and SVM algorithm used for lung cancer detection. Journal of Chemical and Pharmaceutical research. 2016;p. 351–359. Available from: https://www.jocpr.com/abstract/pso-genetic-optimization-and-svm-algorithm-used-for-lung-cancer-detection-5133.html
Yuvarani, Sakthi. Optimization techniques for Lung cancer analysis- A survey”. International journal of pure and applied Mathematics. 2018;118:207–214. Available from: https://acadpubl.eu/jsi/2018-118-7-9/articles/8/28
Priyanka, AK, Sharma K, Saini K. GLCM and its features”. International Journal of Advanced Research in Electronics and Communication Engineering. 2015;4:1–8. Available from: http://ijarece.org/wp-content/uploads/2015/08/IJARECE-VOL-4-ISSUE-8-2180-2182
Swati P, Chakkarwar VA. Classification of lung tumour using SVM”. International Journal of Computational Engineering Research. 2012;2:1254–1257. Available from: http://www.ijceronline.com/papers/Vol2_issue5/N02512541257
Liu P, Xie M, Bian J, Li H, Song L. A Hybrid PSO–SVM Model Based on Safety Risk Prediction for the Design Process in Metro Station Construction. International Journal of Environmental Research and Public Health. 2020;17(5):1714. Available from: https://dx.doi.org/10.3390/ijerph17051714
Simone L, Jakobovic D, Picek S. Classification of cancer data, analyzing gene expression data using a Fuzzy decision tree algorithm. Springer. 2018. Available from: https://link.springer.com/chapter/10.1007/978-3-319-65455-3_13
Moghadas-Dastjerdi H, Ahmadzadeh M, Samani A. Towards computer based lung disease diagnosis using accurate lung air segmentation of CT images in exhalation and inhalation phases. Expert Systems with Applications. 2017;71:396–403. Available from: https://dx.doi.org/10.1016/j.eswa.2016.11.013
Priyadharshini J, Shirdhinkar M. Detection of Lung cancer using SVM Classification”. International research journal of Engineering and Technology. 2017;4:378–381. Available from: https://ieeexplore.ieee.org/abstract/document/8878774
Mithra KS, Emmanuel S, WR. FHDT: Fuzzy and Hyco-entropy-based Decision Tree Classifier for Tuberculosis Diagnosis from Sputum Images. 2018. Available from: https://link.springer.com/article/10.1007/s12046-018-0878-y
Parveen SS, Kavitha C. Classification of Lung Cancer Nodules using SVM Kernels. International Journal of Computer Applications. 2014;95(25):25–28. Available from: https://dx.doi.org/10.5120/16751-7013
Asuntha A, Brindha A, Indirani S, Srinivasan A. Lung cancer detection Using SVM algorithm and optimization techniques. J. Chem. Pharm. Sci. 2016;9(4):3198–3203.
Mohamad MS, Omatu S, Deris S, Yoshioka M. A Constraint and Rule in an Enhancement of Binary Particle Swarm Optimization to Select Informative Genes for Cancer Classification. In: Pacific-Asia Conference on Knowledge Discovery and Data Mining. (pp. 168-178) Springer. 2013.
Tidke PS, Chakkarwar VA. Classification of lung tumor using SVM. International Journal Of Computational Engineering Research. 2012;2(5).

Copyright

© 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)