Fractional crow search-based support vector neural network for patient classification and severity analysis of tuberculosis

Fractional crow search-based support vector neural network for patient classification and severity analysis of tuberculosis

For access to this article, please select a purchase option:

Buy article PDF
(plus tax if applicable)
Buy Knowledge Pack
10 articles for £75.00
(plus taxes if applicable)

IET members benefit from discounts to all IET publications and free access to E&T Magazine. If you are an IET member, log in to your account and the discounts will automatically be applied.

Learn more about IET membership 

Recommend Title Publication to library

You must fill out fields marked with: *

Librarian details
Your details
Why are you recommending this title?
Select reason:
IET Image Processing — Recommend this title to your library

Thank you

Your recommendation has been sent to your librarian.

The world is chasing towards the automation in the severity analysis and classification of the patients based on the severity of tuberculosis (TB). The automatic classification is very much useful for developing countries that are struggling to reduce the fatality rate of the persons suffering from TB as it is a top standing infectious disease. Thus, the automatic classification of the TB patients using the sputum images with the proposed fractional crow search-based support vector neural network is presented. The proposed classification method is the integration of the fractional theory in the crow search algorithm that increases the computational speed and reduces the cost and time spent on analysing the test samples. The importance of the proposed method is that it requires minimum manual power and hence, the inaccuracies are reduced. The experimentation performed using the Ziehl–Neelsen sputum smear microscopy image database proves that the proposed classifier is highly accurate and offered an improved performance in terms of accuracy rate, true positive rate, and false-positive rate.


    1. 1)
      • 1. Firdaus, S., Kaur, I.R., Kashyap, B., et al: ‘Front loading sputum microscopy – an alternative approach for diagnosis of pulmonary tuberculosis’, J. Clin. Tuberc. Other Mycobacterial Dis., 2017, 8, pp. 612.
    2. 2)
      • 2. Hussainy, S.F., Zaffar, F., Zaffar, M.A., et al: ‘Decision-tree inspired classification algorithm to detect Tuberculosis (TB)’. Proc. PACIS, Langkawi, 2017.
    3. 3)
      • 3. Bhutia, R., Narain, K., Devi, K.R., et al: ‘Direct and early detection of Mycobacterium tuberculosis complex and rifampicin resistance from sputum smears’, Int. J. Tuberc. Lung Dis., 2013, 17, (2), pp. 258261.
    4. 4)
      • 4. Priya, E., Srinivasan, S.: ‘Automated object and image level classification of TB images using support vector neural network classifier’, Biocybernetics Biomed. Eng., 2016, 36, (4), pp. 670678.
    5. 5)
      • 5. Steingart, K.R., Henry, M., Ng, V.: ‘Fluorescence versus conventional sputum smear microscopy for tuberculosis: a systematic review’, Lancet Infect. Dis., 2006, 6, (9), pp. 570581.
    6. 6)
      • 6. Patel, B., Douglas, T.S.: ‘Creating a virtual slide map from sputum smear images for region-of-interest localisation in automated microscopy’, Comput. Methods Programs Biomed., 2012, 108, (1), pp. 3852.
    7. 7)
      • 7. Mozos, R.S., Pérez-Cruz, F., Madden, M.G., et al: ‘An automated screening system for tuberculosis’, IEEE. J. Biomed. Health. Inform., 2014, 18, (3), pp. 855862.
    8. 8)
      • 8. Pai, M., Culloch, M.M., Enanoria, W., et al: ‘Systematic reviews of diagnostic 579 test evaluations: what's behind the scenes’, Evidence Based Med., 2004, 9, pp. 101103.
    9. 9)
      • 9. Mutingwende, I., Vermeulen, U., Steyn, F., et al: ‘Development and evaluation of a rapid multiplex-PCR based system for Mycobacterium tuberculosis diagnosis using sputum samples’, J. Microbiol. Methods, 2015, 116, pp. 3743.
    10. 10)
      • 10. Veropoulos, K., Learmonth, G., Campbell, C., et al: ‘The automated identification of tubercle bacilli in sputum: a preliminary investigation’, Anal. Quant. Cytol. Histol., 1999, 21, (4), pp. 277281.
    11. 11)
      • 11. Forero, M.G., Cristobal, G., Desco, M.: ‘Automatic identification of mycobacterium tuberculosis by Gaussian mixture models’, J. Microsc., 2006, 223, (2), pp. 120132.
    12. 12)
      • 12. WHO: ‘Treatment of tuberculosis: guidelines’ (World Health Organization, Geneva, Switzerland, 2009, 4th edn.).
    13. 13)
      • 13. Zhai, Y., Liu, Y., Zhou, D., et al: ‘Automatic identification of mycobacterium tuberculosis from ZN-stained sputum smear: algorithm and system design’. Proc. 2010 IEEE Int. Conf. on Robotics and Biomimetics, Tianjin, China, 2010.
    14. 14)
      • 14. Ebenezer, P., Subramanian, S.: ‘Separation of overlapping bacilli in microscopic digital TB images’, Biocybernetics Biomed. Eng., 2015, 35, (2), pp. 8799.
    15. 15)
      • 15. Stipic-Markovic, C.M., Kardum-Skelin, A., Stipic, I., et al: ‘Induced sputum: a method for cytologic analysis of bronchial specimens’, Acta Clin. Croatica, 2002, 41, pp. 8993.
    16. 16)
      • 16. Veropoulos, K., Learmonth, G., Campbell, C., et al: ‘Automatic identification of tubercle bacilli in sputum. A preliminary investigation’, Anal. Quant. Cytol. Histol., 1999, 21, (4), pp. 277281.
    17. 17)
      • 17. Duan, Z., Wang, Q.L.S., Chen, X., et al: ‘Identification of Mycobacterium tuberculosis PPE68-specific HLA-A*0201-restricted epitopes for tuberculosis diagnosis’, Curr. Microbiol., 2015, 70, (6), pp. 769778.
    18. 18)
      • 18. Foreroa, M.G., Sroubek, F., Bala, G.C.: ‘Identification of tuberculosis bacteria based on shape and color’, Real-Time Imaging, 2004, 10, pp. 251262.
    19. 19)
      • 19. Nayak, R., Shenoy, V.P., Galigekere, R.R.: ‘A new algorithm for automatic assessment of the degree of TB-infection using images of ZN-stained sputum smear’. Int. Conf. on Systems in Medicine and Biology, IIT Kharagpur, India, 16–18 December 2010.
    20. 20)
      • 20. Omisorea, M.O., Samuel, O.W., Atajeromavwoc, E.J.: ‘A genetic-neuro-fuzzy inferential model for diagnosis of tuberculosis’, Appl. Comput. Inf., 2017, 13, (1), pp. 2737.
    21. 21)
      • 21. Ndubuisi, N.O., Azuonye, O.R., Victor, N.O., et al: ‘Front-loaded sputum microscopy in the diagnosis of pulmonary tuberculosis’, Int. J. Mycobacteriol., 2016, 5, (4), pp. 489492.
    22. 22)
      • 22. Orhan, E., Temurtas, F., Tanrıkulu, A.Ç.: ‘Tuberculosis disease diagnosis using artificial neural networks’, J. Med. Syst., 2010, 34, (3), pp. 299302.
    23. 23)
      • 23. Khutlang, R., Krishnan, S., Dendere, R., et al: ‘Classification of Mycobacterium tuberculosis in images of ZN-stained sputum smears’, IEEE Trans. Inf. Technol. Biomed., 2010, 14, (4), pp. 949957.
    24. 24)
      • 24. Ayas, S., Ekinci, M.: ‘Random forest-based tuberculosis bacteria classification in images of ZN-stained sputum smear samples’, Signal Image Video Process., 2014, 8, (1), pp. 4961.
    25. 25)
      • 25. Bradley, D., Roth, G.: ‘Adaptive thresholding using the integral image’, J. Graph. Tools, 2011, 12, (2), pp. 1321.
    26. 26)
      • 26. Mary, A., Chacko, M.O., Dhanya, P.M.: ‘A comparative study of different feature extraction techniques for offline Malayalam character recognition’, Comput. Intell. Data Min., 2014, 2, pp. 918.
    27. 27)
      • 27. Sergyan, S.: ‘Color histogram features based image classification in content-based image retrieval systems’. Proc. 6th Int. Symp. on Applied Machine Intelligence and Informatics, Herlany, Slovakia, 2008, pp. 221224.
    28. 28)
      • 28. Jun, B., Choi, I., Kim, D.: ‘Local transform features and hybridization for accurate face and human detection’, IEEE Trans. Pattern Anal. Mach. Intell., 2013, 35, (6), pp. 14231436.
    29. 29)
      • 29. Bhaladhare, P.R., Jinwala, D.C.: ‘A clustering approach for the -diversity model in privacy preserving data mining using fractional calculus-bacterial foraging optimization algorithm’, Adv. Comput. Eng., 2014, pp. 112.
    30. 30)
      • 30. Askarzadeh, A.: ‘A novel metaheuristic method for solving constrained engineering optimization problems: crow search algorithm’, Comput. Struct., 2016, 169, pp. 112.
    31. 31)
      • 31. Yadav, P.: ‘Document-Document similarity matrix and Naive-Bayes classification to web information retrieval’, Int. J. Eng. Res. Gen. Sci., 2014, 2, (6), pp. 10581064.
    32. 32)
      • 32. Ziehl–Neelsen: ‘Ziehl–Neelsen sputum smear microscopy image database (ZNSM-iDB)’, 2017. Available at
    33. 33)
      • 33. Ludwig, O., Nunes, U., Araujo, R.: ‘Eigen value decay: a new method for neural network regularization’, Neurocomputing, 2014, 124, pp. 3342.

Related content

This is a required field
Please enter a valid email address