Lung Nodule Detection of CT and Image-Based GLCM and RLM CT Scan Using the Support Vector Machine (SVM) Method
DOI:
https://doi.org/10.12962/jaree.v5i2.125Abstract
Lung cancer is the most common cause of cancer death globally. Early detection of lung cancer will greatly beneficial to save the patient. This study focused on the detection of lung cancer using classification with the Support Vector Machine (SVM) method based on the features of Gray Level Co-occurrence Matrices (GLCM) and Run Length Matrix (RLM). The lung data used were obtained from the Cancer imaging archive Database, consisting of 500 CT images. CT images were grouped into 2 clusters, including normal and lung cancer. The research steps include: image processing, region of interest segmentation, and feature extraction. The results indicate that the system can detect the CT-image of SVM classification where the default parameter only provides an accuracy of 85.63%. It is expected that the results will be useful to help medical personnel and researchers to detect the status of lung cancer. These results provide information that detection of lung nodules based on GLCM and RLM features that can be detected is better. Furthermore, selecting parameters C and γ on SVM.
Keywords: cancer, nodule, support vector machine (SVM).
References
American Cancer Society. (2013). What are the risk factors for non-smallcell lung cancer?. 19 Desember 2013. <http://www.cancer.org>.
Ancuceanu, R. V., and Victoria, I, 2004, Pharmacologically Active Natural Compounds for Lung Cancer, Altem. Med. Rev., 9 ,4 ,4 0 2 -4 1 9
M.S.Tarawneh, “Lung Cancer Detection Using Image Processing Techniques”, Leonardo Electronic Journal of Practices and Technologies, Issue 20, pp. 147-158, January-June 2012.
Kusumadewi, S.. Membangun Jaringan Syaraf Tinian dengan Matlab dan Exel Link. Yogyakarta: Graha Emu.
Cleveland Clinic Medical Professional (2021). Pulmonary Nodules. 28 May 2021. <https://my.clevelandclinic.org/health/diseases_conditions/hic_Pulmonary_Nodules>
Vinod kumar, Kanwal Garg,” Neural network based approach for detection of abnormal regions of lung cancer in x ray”, International journal of engineering research & Technology (IJERT), ISSN:2278-0181, vol 1 Issue 5, July-2012 p 1-7
S. Singh, R. Vijay, Y. Singh, “Artificial Neural Network and Cancer Detection”, IOSR Journal o f Computer Engineering, pp. 20-24.
F. Taher, N. Werhi, H. Al-Ahmad, R. Sammouda, “Lung Cancer Detection by Using Artificial Neural Network and Fuzzy Clustering Methods”, American Journal of Biomedical Engineering 2012, 2(3), pp. 136-142.
Almas Pathan, Bairu.K.saptalkar, “Detection and Classification o f Lung Cancer Using Artificial Neural Network”, International Journal on Advanced Computer Engineering and Communication Technology Vol-1 Issue:1
A. Badnjevic, M. Cifiek, D. Koraga, D. Osmankovic, “Neuro-fiizzy classification o f asthma and chronic obstructive pulmonary disease”, BMC Med Inform Decis Mak. 2015; 15(Suppl 3): SI. Published online 2015 Sep 11.
A. O. De Carvalho Filho, W. B. De Sampaio, A. C. Silva, A. C. de Paiva, R. A. Nunes, and M. Gattass, “Automatic detection of solitary lung nodules using quality threshold clustering, genetic algorithm and diversity index,” Artif. Intell. Med., vol. 60, no. 3, pp. 165–177, 2014.
R.M.Haralick, K. Shanmugam, R.M.Haralick, K. Shanmugam,, ‘Textural features for image classification,” IEEE Trans. System Man. Cybernetics, vol. SMC-3, pp. 610-621,1973
D. Tian, “A Review on Image Feature Extraction and Representation Techniques”, International Journal o f Multimedia and Ubiquitous Engineering Vol. 8, No. 4, July, 2013, pp. 385-396.
Nurtanio, Inggrid, Astuti, E.R.,Purnama, I.K.E., Hariadi, M., Purnomo, M. H., Classifying Cyst and Tumor Lesion Using Support Vector Machine Based on Dental Panoramic Images Texture Feature, IAENG International Journal of Computer Science, Vol. 40, No.1, Feb 2013.
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