An Efficient Machine Learning Approach for Screening of COPD Lung Disease

(*) Corresponding author

Authors' affiliations

DOI's assignment:
the author of the article can submit here a request for assignment of a DOI number to this resource!
Cost of the service: euros 10,00 (for a DOI)


Chronic Obstructive Pulmonary Disease (COPD) is the fourth leading cause of death worldwide and the only chronic disease with increasing mortality rates. Progression of the disease is irreversible but can be stabilized or prevented by quitting. If COPD is detected earlier, the formation of lung cancer is prevented. In CT scan may provide additional information and also it provides more detailed images of parts of the body that cannot easily be seen on a standard chest radiograph. But the automatic screening process has lot of advantages such as decrease of labor, enhancing the sensitivity of the test and better precision in diagnosis by increasing the images that can be analyzed by the computer. Many researchers have proposed different techniques to improve the performance of automatic screening process. This paper involves in improving the accuracy over the existing technique using the adaptive region growing property and Extreme Learning Machine (ELM) classifier. Initially, pre-processing is carried out for the input image by Laplacian Gaussian filtering technique to make the image suitable for further processing. The contours of the image will be obtained using region growing technique. The ELM classifier is then used to confirm the suspected TB cavities. The classification will be carried out by the features which have been taken from the segmented image. The proposed technique is implemented in MATLAB and the performance is compared with the existing technique. From the experimental result it can be said that the proposed method achieved more accuracy as compared with existing techniques.
Copyright © 2013 Praise Worthy Prize - All rights reserved.


Chronic Obstructive Pulmonary Disease; ELM Classifier; Laplacian Gaussian Filtering; Local Gabor XOR Pattern (LGXP)

Full Text:



Pauwels RA, Rabe KF. Burden and clinical features of chronic obstructive pulmonary disease (COPD). Lancet 2004; 364: 613–620.

Murray CJ, Lopez AD. Alternative projections of mortality and disability by cause 1990–2020: Global Burden of Disease Study. Lancet 1997; 349: 1498–1504.

Pauwels RA, Buist AS, Calverley PM, Jenkins CR, Hurd SS, GOLD Scientific Committee. Global strategy for the diagnosis, management, and prevention of chronic obstructive pulmonary disease. NHLBI/WHO Global Initiative for Chronic Obstructive Lung Disease (GOLD) workshop summary. Am J Respir Crit Care Med 2001; 163: 1256–1276.

GOLD Guidelines 2003. Date last accessed: December 2 2005.

Stang P, Lydick E, Silberman C, Kempel A, Keating ET. The prevalence of COPD: using smoking rates to estimate disease frequency in the general population. Chest 2000; 117: Suppl. 2, 354S–359S.

Sobradillo-Pena V, Miravitlles M, Gabriel R, et al. Geographic variations in prevalence and underdiagnosis of COPD: results of the IBERPOC multicentre epidemiological study. Chest 2000; 118: 981–989.

Fukuchi Y, Nishimura M, Ichinose M, et al. COPD in Japan: the Nippon COPD epidemiology study. Respirology 2004; 9: 458–465.

Rennard S, Decramer M, Calverley PM, et al. Impact of COPD in North America and Europe in 2000: subjects’ perspective of Confronting COPD International Survey. Eur Respir J 2002; 20: 799–805.

Viegi G, Scognamiglio A, Baldacci S, Pistelli F, Carrozzi L. Epidemiology of chronic obstructive pulmonary disease (COPD). Respiration 2001; 68: 4–19.

S. Boucherkha, Z. Laboudi, M. Benmohamed, A Low Cost Multipurpose Algorithm for Secure Transfer of Medical Images, (2006) International Review on Computers and Software (IRECOS), 1 (3), pp. 217 - 223.

B. Magesh, P. Vijayalakshmi, M. Abirami, “Computer Aided Diagnosis System For the Identification and Classification of Lessions in Lungs”, International Journal of Computer Trends and Technology- May to June Issue 2011.

V.M. Katoch, “Newer diagnostic techniques for tuberculosis”, Central JALMA Institute for Leprosy & Other Mycobacterial Diseases (ICMR), Agra, India Received July 16, 2003.

Giger, M.L., N. Karssemeijer, and S.G. Armato, Guest editorial computer- aided diagnosis in medical imaging, pp. 1205-1208.

Y.Uchiyama, S.Katsuragawa, H.Abe, J.Shiraishi, F.Li, Q.Li, C.-T.Zhang, K.Suzuki and K.Doi. Quantitive computerised analysis of diffuse lung disease in high-resolution computed tomography, Med. Phys. 30 (9) September 2003, pp. 2440-2453

Sluimer IC, van Waes PF, Viergever MA, van Ginneken B. Computer-aided diagnosis in high resolution CT of the lungs, Med Phys. 30 (12) December 2003, pp. 3081-90.

N. Lee et al., “Fatty and fibroglandular tissue volumes in the breasts of women 20-83 years old: Comparison of X-ray mammography and computer-assisted MR imaging,” Amer. J. Roentgenol ., vol. 168, pp. 501–506, 1997.

Shufu Xie, Shiguang Shan, Xilin Chen, Jie Chen, “Fusing Local Patterns of Gabor Magnitude and Phase for Face Recognition”, IEEE Transactions on Image Processing, Vol. 19, No. 5, PP. 1349-1361, 2010.

S. Karpagachelvi, Dr. M. Arthanari and M.Sivakumar,” Classification of Electrocardiogram Signals With Extreme Learning Machine and Relevance Vector Machine”, IJCSI International Journal of Computer Science Issues, Vol. 8, Issue 1, January 2011.

M. H. Malik, S. A. M. Gilani, Anwaar-ul-Haq, Adaptive Image Fusion Scheme Based on Contourlet Transform and Machine Learning, (2008) International Review on Computers and Software (IRECOS), 3 (1), pp. 62 – 69.

S. Loveymi, B. Shadgar, A. Osareh, An Efficient Approach to Automated Medical Image Annotation, (2011) International Review on Computers and Software (IRECOS), 6 (5), pp. 749-759.


  • There are currently no refbacks.

Please send any question about this web site to
Copyright © 2005-2023 Praise Worthy Prize