Advertisement

Malaria Detection and Classification Using Machine Learning Algorithms

  • Yaecob Girmay Gezahegn
  • Yirga Hagos G. Medhin
  • Eneyew Adugna Etsub
  • Gereziher Niguse G. Tekele
Conference paper
Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST, volume 244)

Abstract

Malaria is one of the most infectious diseases, specifically in tropical areas where it affects millions of lives each year. Manual laboratory diagnosis of Malaria needs careful examination to distinguish infected and healthy Red Blood Cells (RBCs). However, it is time consuming, needs experience, and may face inaccurate lab results due to human errors. As a result, doctors and specialists are likely to provide improper prescriptions. With the current technological advancement, the whole diagnosis process can be automated. Hence, automating the process needs analysis of the infected blood smear images so as to provide reliable, objective result, rapid, accurate, low cost and easily interpretable outcome. In this paper comparison of conventional image segmentation techniques for extracting Malaria infected RBC are presented. In addition, Scale Invariant Feature Transform (SIFT) for extraction of features and Support Vector Machine (SVM) for classification are also discussed. SVM is used to classify the features which are extracted using SIFT. The overall performance measures of the experimentation are, accuracy (78.89%), sensitivity (80%) and specificity (76.67%). As the dataset used for training and testing is increased, the performance measures can also be increased. This technique facilitates and translates microscopy diagnosis of Malaria to a computer platform so that reliability of the treatment and lack of medical expertise can be solved wherever the technique is employed.

Keywords

Machine learning Image segmentation SIFT SVM Blood smear Microscopic Feature extraction 

References

  1. 1.
    WHO: Global report on antimalarial efficacy and drug resistance (2000–2010)Google Scholar
  2. 2.
    Korenromp, E., et al.: World malaria report. World Health Organization, Geneva, Technical report (2005)Google Scholar
  3. 3.
    Gallup, J., Sachs, J.: The economic burden of malaria. J. Trop. Med. 64, 85–96 (2001)Google Scholar
  4. 4.
    National Centers for Disease Control Prevention: Laboratory identification of parasites of public health concern. Division of Parasitic Diseases. Accessed 4 Jan 2017Google Scholar
  5. 5.
    Coatney, G., et al.: The primate malarias. U.S. Department of Health, Education and Welfare (1971)Google Scholar
  6. 6.
  7. 7.
    Microsoft Corporation: Microsoft encarta encyclopedia (2002)Google Scholar
  8. 8.
    Sherman, I.W.: Malaria: parasite biology, pathogenesis and protection (1998)Google Scholar
  9. 9.
    Kareem, S., et al.: A novel method to count the red blood cells in thin blood films. In: IEEE International Symposium on Circuits and Systems (ISCAS), pp. 1021–1024 (2011)Google Scholar
  10. 10.
    Kareem, S., et al.: Automated malaria parasite detection in thin blood films: a hybrid illumination and color constancy insensitive, morphological approach. Applied DSP and VLSI Research Group, University of Westminster London, United Kingdom (2012)Google Scholar
  11. 11.
    Zhiming, T.: Research on graph theory based image segmentation and its embedded application, pp. 14–24. Dissertation of Shanghai Jiao Tong University, Shanghai (2007)Google Scholar
  12. 12.
  13. 13.
    Acharya, T., Ray, A.K.: Image Processing Principles and Applications. Wiley, Hoboken (2005). Arizona State University, TempeCrossRefGoogle Scholar
  14. 14.
    WHO: New perspectives, malaria diagnosis. World Health Organization, Geneva, Technical report (2000)Google Scholar
  15. 15.
    Tek, F.B.: Computerized diagnosis of malaria. Ph.D. thesis, University of Westminster, September 2007Google Scholar
  16. 16.
    Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis. 60(2), 91–110 (2004)CrossRefGoogle Scholar
  17. 17.
    Cortes, C., Vapnik, V.: Support-vector networks. Mach. Learn. 20, 273–297 (1995)MATHGoogle Scholar
  18. 18.
    Kareem Reni, S.: Automated low-cost malaria detection system in thin blood slide images using mobile phones. Doctoral thesis, University of Westminster, March 2014Google Scholar
  19. 19.

Copyright information

© ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2018

Authors and Affiliations

  • Yaecob Girmay Gezahegn
    • 1
  • Yirga Hagos G. Medhin
    • 2
  • Eneyew Adugna Etsub
    • 1
  • Gereziher Niguse G. Tekele
    • 2
  1. 1.Addis Ababa UniversityAddis AbabaEthiopia
  2. 2.Mekelle UniversityMekelleEthiopia

Personalised recommendations