Texture Filter Optimization Using Particle Swarm Optimization for Efficient Lung Image Classification

Main Article Content

S.Vatchala
Sridevi.R
A. Sathish
S. Ravimaran
M. Nallusamy

Keywords

Medical Images, Gabor filter, Feature selection, Particle Swarm Optimization (PSO), Naïve Bayes and Random Tree

Abstract

There are several millions of images that have been generated by the hospitals and healthcare centres on a daily basis. Imaging was employed as the preferred tool of diagnostics by many more medical procedures. Detecting lung cancer at an early stage can to a significant extent enhance survival chances but can also be challenging to detect the various stages of lung cancer with a lesser number of symptoms. Medical image processing is favoured by Gabor filter and for the application of signal processing, it is a sharp cut-off. A step in pre-processing is the next step in machine learning and this is very effective in the reduction of dimensionality and removal of irrelevant data. It also helps in increasing accuracy and in learning accuracy improvement aside from the comprehensibility of results. Particle Swarm Optimization (PSO) has been used widely for solving problems in optimization which is a problem of feature selection. For the PSO, every solution has been looked at as a particle and the algorithm looks for an ideal solution taking into consideration all particles.

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References

1. Lakshmanaprabu, S. K., Mohanty, S. N., Shankar, K., Arunkumar, N., & Ramirez, G. (2019). Optimal deep learning model for classification of lung cancer on CT images. Future Generation Computer Systems, 92, 374-382. 2. Smitha, P., Shaji, L., & Mini, M. G. (2011). A review of medical image classification techniques. In International conference on VLSI, Communication &Intrumrnataiom (pp. 34-38).
3. BahriyeAkay,” A Study on Partile Swarm Optimization and Artifical Bee Colony algorithms for Multilevel Thresholding”, Elseiver June 2013.
4. R.Sridevi, DR.K.Lakshmi,(2010),”Signature Analysis of UDP Streams for Intrusion Detection using Data mining Algorithms” International Journal on Computer Science and Engineering, Vol. 02, No. 07, 2010, Page(s): 2461-2465.
5. R. Sridevi and Dr.RajanChattamvelli,” Performance Evaluation of Artificial Immune
System based Classifiers in Intrusion Detection”, International Journal of Computational Intelligence Research, ISSN 0973-1873 Volume 7, Number 3 (2011), pp. 247-253. 6. Tun, K. M. M., &Soe, K. A. (2014). Feature extraction and classification of lung cancer nodule using image processing techniques. International Journal of Engineering Research & Technology (IJERT), 3(3). 7. Alakwaa, W., Nassef, M., &Badr, A. (2017). Lung cancer detection and classification with 3D convolutional neural network (3D-CNN). Lung Cancer, 8(8), 409.
8. R.Sridevi, S.Srimathi,(2020),” Populating Indian GST Details Into Java Apache Derby Database Powered By Glass Fish Server” Inventive Communication and Computational Technologies, springer series, Page(s): 409-421.
9. R.Sridevi, N.Nithya,(2020),” Intrusion Detection System Using Wosad Method” Inventive Communication and Computational Technologies, springer series, Page(s): 423-430. 10. Kaur, T., & Gupta, E. N. (2015). Classification of Lung Diseases Using Optimization Techniques. Int. J. Sci. Res. Dev, 3(8), 852-854. 11. Senthil Kumar, K., Venkatalakshmi, K., &Karthikeyan, K. (2019). Lung Cancer Detection Using Image Segmentation by means of Various Evolutionary Algorithms. Computational and mathematical methods in medicine, 2019. 12. Arivazhagan, S., &Benitta, R. (2013, February). Texture classification using color local texture features. In Signal Processing Image Processing & Pattern Recognition (ICSIPR), 2013 International Conference on (pp. 220-223). IEEE. 13. Jain, I., Jain, V. K., & Jain, R. (2018). Correlation feature selection based improved-binary particle swarm optimization for gene selection and cancer classification. Applied Soft Computing, 62, 203-215. 14. R.Sridevi ,S.Srimathi , (2019), "Dynamic Malware Attack Detection And Prevention In Real Time IoT With Hybrid Signature Free Method", IJIERT - International Journal of Innovations in Engineering Research and Technology, Volume 6, Issue 6, ISSN : 2394-3696, Page No. 48-56. 15. Nithya, T.M., Chitra, S. Soft computing-based semi-automated test case selection using gradient-based techniques. Soft Comput 24, 12981–12987 (2020).
16. Yan X, Wu Q, Liu H, Huang W (2013) An improved particle swarm optimization algorithm and its application. Int J ComputSci Issues (IJCSI) 10(1):316–324.
17. Mahdi Setayesh, Mengjie Zhang, Mark Johnston,” A Novel Particle Optimization Approch to Detecting Continuous, Thin and Smooth Edges in noisy images”, Volume 246, Elseiver 10 Oct 2013.
18. Amoozegar, M., &Minaei-Bidgoli, B. (2018). Optimizing multi-objective PSO based feature selection method using a feature elitism mechanism. Expert Systems with Applications, 113, 499-514. 19. Dr.R.Sridevi, R.Annakamachi, J.Nandhini, M.S.Nithasha,” Personalized Password Protection in College Level Inter/Intra Mural Sports Events”, StudiaRosenthaliana (Journal for the Study of Research), Volume XII, Issue III, March-2020, ISSN NO: 1781-7838, Page(s): 60-69.
20. Mishra., A, K, and Ratha., B, K (2016) “Study of Random Tree and Random Forest Data Mining Algorithms for Microarray Data Analysis”, International Journal on Advanced Electrical and Computer Engineering (IJAECE), Volume -3, Issue -4, 2016, pp (5-7).
21. Amudha L., Pushpalakshmi R. (2021) Applications, Analytics, and Algorithms—3 A’s of Stream Data: A Complete Survey. In: Peter J., Fernandes S., Alavi A. (eds) Intelligence in Big Data Technologies—Beyond the Hype. Advances in Intelligent Systems and Computing, vol 1167(599-606). Springer, https://doi.org/10.1007/978-981-15-5285-4_60.