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Software-based Diagnostic Approach for Detection of Malaria Parasite in Blood

by Adetokunbo A. Adenowo, Adesoji A. Awobajo, Sheriff Alimi
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 175 - Number 12
Year of Publication: 2020
Authors: Adetokunbo A. Adenowo, Adesoji A. Awobajo, Sheriff Alimi
10.5120/ijca2020920600

Adetokunbo A. Adenowo, Adesoji A. Awobajo, Sheriff Alimi . Software-based Diagnostic Approach for Detection of Malaria Parasite in Blood. International Journal of Computer Applications. 175, 12 ( Aug 2020), 17-23. DOI=10.5120/ijca2020920600

@article{ 10.5120/ijca2020920600,
author = { Adetokunbo A. Adenowo, Adesoji A. Awobajo, Sheriff Alimi },
title = { Software-based Diagnostic Approach for Detection of Malaria Parasite in Blood },
journal = { International Journal of Computer Applications },
issue_date = { Aug 2020 },
volume = { 175 },
number = { 12 },
month = { Aug },
year = { 2020 },
issn = { 0975-8887 },
pages = { 17-23 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume175/number12/31504-2020920600/ },
doi = { 10.5120/ijca2020920600 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:24:50.459781+05:30
%A Adetokunbo A. Adenowo
%A Adesoji A. Awobajo
%A Sheriff Alimi
%T Software-based Diagnostic Approach for Detection of Malaria Parasite in Blood
%J International Journal of Computer Applications
%@ 0975-8887
%V 175
%N 12
%P 17-23
%D 2020
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Malaria is a serious global health problem. Its diagnosis is prevalently done manually using conventional compound light microscopy. However, this traditional approach is time consuming, tiresome, gives variation in results and requires skilled personnel which may not be available everywhere and anytime. To overcome these challenges and provide a reliable alternative, a software-based approach is proposed. The approach is underpinned by image analysis techniques; it aims the detection and diagnosis (or screening) of malaria infection in microscopic images of stained thin blood film smears. Thus, the proposed approach combines selected pre-processing, segmentation, feature extraction and edge detection schemes to distinguish malaria cells in order to identify malaria parasites in stained plasmodium images. Ninety-two (92) images of infected and non-infected plasmodium parasites were acquired (in three categories: downloaded, snapped and digitally acquired images), pre-processed, segmented, relevant features extracted and diagnosis made based on features extracted from the images. The accuracy of the approach was tested. Results show that this approach achieved 91.3% accuracy level, 96.6% sensitivity, and 94.4% positive predictive value. The level of outcome suggests that the software approach can be successfully used for malaria detection.

References
  1. World Health Organisation. (2019). World malaria report. Geneva: World Health Organization, December, 2019.
  2. World Health Organisation. (2018). World malaria report.
  3. Conroy, A.L., Datta, D. and John, C.C. (2019). What causes severe malaria and its complications in children? Lessons learned over the past 15 years. BMC Med 17, 52. https://doi.org/10.1186/s12916-019-1291-z.
  4. Mukry, S. N., Saud, M., Sufaida, G., Shaikh, K., Naz, A. and Shamsi, T. S. (2017). Laboratory diagnosis of malaria: comparison of manual and automated diagnostic tests. Canadian Journal of Infectious Diseases and Medical Microbiology, 2017.
  5. Wongsrichanalai, C., Barcus, M. J., Muth, S., Sutamihardja, A. and Wernsdorfer, W. H. (2007). A review of malaria diagnostic tools: microscopy and rapid diagnostic test (RDT). The American journal of tropical medicine and hygiene, 77(6_Suppl), 119-127.
  6. Okete, J. A., Oden, E. M. and Adofikwu, C. E. (2018). Reliability of Urine Malaria Test (UMT) for Malaria Diagnosis. Asian Journal of Research in Medical and Pharmaceutical Sciences, 1-9.
  7. Oguonu, T., Shu, E., Ezeonwu, B. U., Lige, B., Derrick, A., Umeh, R. E. and Agbo, E. (2014). The performance evaluation of a urine malaria test (UMT) kit for the diagnosis of malaria in individuals with fever in south-east Nigeria: cross-sectional analytical study. Malaria Journal, 13(1), 403.
  8. Yin, J., Li, M., Yan, H. and Zhou, S. (2018). Considerations on PCR-based methods for malaria diagnosis in China malaria diagnosis reference laboratory network. Bioscience trends, 12(5), 510-514.
  9. World Health Organization. (1991). Basic Malaria Microscopy Part I. Learner’s Guide World Health Organization, WHO, Geneva, Switzerland.
  10. Linder, N., Turkki, R., Walliander, M., Mårtensson, A., Diwan, V., Rahtu, E.,.......and Lundin, J. (2014). A malaria diagnostic tool based on computer vision screening and visualization of Plasmodium falciparum candidate areas in digitized blood smears. PLoS One, 9(8), e104855.
  11. Asha, T., Deepak, R., Swarooparadhya, M.C., Aishwarya, M.G. and Geetha, B.S. (2016). Automated Malaria Parasite Detection based on Image Processing. International Journal for Research in Technological Studies, Vol. 3, Issue 2, January 2016 , ISSN (online): 2348-1439
  12. Diaz, G., Gonzalez, F.A. and Eduardo, R. (2009). A semi-automatic method for quantification and classification of erythrocytes infected with malaria parasites in microscopic images. Journal of Biomedical Informatics 42, 296-307.
  13. Anand, A., Chhaniwal, V.K., Patel, N.R. and Javidi, B. (2012). Automatic identification of malaria infected red blood cell (RBC) with digital holographic microscopy using correlation algorithms. Journal of Photonics, IEEE, 2012.2210199/1943-0655.
  14. Bashir, A., Mustafa, Z.A., Abdelhameid, I. and Ibrahem, R. (2017). Detection of malaria parasites using digital image processing. 2017 International Conference on Communication, Control, Computing and Electronics Engineering (ICCCCEE), Khartoum, 2017, pp. 1-5. doi: 10.1109/ICCCCEE.2017.7867644.
  15. Stratton, L., O'Neill, M. S., Kruk, M. E. and Bell, M. L. (2008). The persistent problem of malaria: Addressing the fundamental causes of a global killer. Social science & medicine, 67(5), 854-862.
  16. Ethiopian Federal Ministry of HEALTH. (2012). Manual for the Laboratory Diagnosis of Malaria. Ethiopian Health and Nutrition Research Institute (EHNRI), Vol. 1, September, 2012.
  17. Manescu, P., Shaw, M.J., Elmi, M., Neary‐Zajiczek, L., Claveau, R., Pawar, V., Kokkinos, I., Oyinloye, G., Bendkowski, C., Oladejo, O.A. and Oladejo, B.F., et al. (2020). Expert‐Level Automated Malaria Diagnosis on Routine Blood Films with Deep Neural Networks. American Journal of Hematology, 1-9. DOI: 10.1002/ajh.25827.
  18. Mens, P.F., Bes, H.M., de Sondo, P., Laochan, N., Keereecharoen, L., Amerongen, A., van Flint, J., Sak, J.R.S., Proux, S., Tinto, H. and Schallig, H.D.F.H. (2012). Direct blood PCR in combination with nucleic acid lateral flow immunoassay for detection of Plasmodium species in settings where malaria is endemic. J. Clin. Microbiol. 50, 3520–3525.
  19. Parija, S. C. (2010). PCR for diagnosis of malaria. Indian J Med Res, July 2010.
  20. Johnston, S.P., Pieniazek, N.J., Xayavong, M.V., Slemenda, S.B., Wilkins, P.P. and Silva, A.J. da (2006). PCR as a confirmatory technique for laboratory diagnosis of malaria. J. Clin. Microbiol. 44, 1087–1089.
  21. Barber, B.E., William, T., Grigg, M.J., Piera, K., Yeo, T.W. and Anstey, N.M. (2013). Evaluation of the sensitivity of a pLDH-based and an aldolase-based rapid diagnostic test for diagnosis of uncomplicated and severe malaria caused by PCR-confirmed Plasmodium knowlesi, Plasmodium falciparum, and Plasmodium vivax. J. Clin. Microbiol. 51, 1118–1123.
  22. Ameh, J., Ahmad, R. M., Ekeh, N., Linga, P., Mangoro, Z., Imam, A. U.,..... and Hudu, S. (2012). Laboratory diagnosis of malaria: Comparing giemsa stained thick blood films with rapid diagnostic test (RDT) in an endemic setting in North-west Nigeria. Journal of Medical Laboratory and Diagnosis, 3(2), 10-15.
  23. Amir, A., Cheong, F. W., De Silva, J. R. and Lau, Y. L. (2018). Diagnostic tools in childhood malaria. Parasites & vectors, 11(1), 53.
  24. World Health Organization. (2009). Basic malaria microscopy: Learners Guide (2nd ed.). Switzerland, 2009.
  25. CDC. (2012). CDC-Malaria-About Malaria-Biology-Malaria Parasites. USA Government. [Online]. Available: http://www.cdc.gov/malaria/about/biology/parasites.html.
  26. Punitha, S., Logeshwari, P., Priyanka, S. and Sivaranjani, P. (2017). Detection of Malarial Parasite in Blood Using Image Processing. Asian Journal of Applied Science and Technology (AJAST), 1(2), 211-213.
  27. Balaji, T. and Sumathi, M. (2018). MRI Based Brain Tumour Edge Detection using Various Edge Detection Techniques. EDITORS OF SPECIAL ISSUE JOURNAL, 135.
  28. MATLAB image edges detection methods, https://www.mathworks.com/matlabcentral/answers/48969-intermediate-result-using-edge-canny
  29. MathWorks image edge detection methods, https://in.mathworks.com/help/images/ref/edge.html#buo5g3x-1.
  30. Nikolic, M., Tuba, E. and Tuba, M. (2016). Edge detection in medical ultrasound images using adjusted Canny edge detection algorithm," 24th Telecommunications Forum (TELFOR), Belgrade, Serbia, November 22 - 23, 2016,1-4. doi: 10.1109/TELFOR.2016.7818878.
  31. Hussain, M. and Bora, D.J. (2018). An Analytical Study on Different Image Segmentation Techniques for Malaria Parasite Detection. 2018 International Conference on Research in Intelligent and Computing in Engineering (RICE), San Salvador, 2018, pp. 1-7, doi: 10.1109/RICE.2018.8509068.
  32. Salamah, U., Sarno, R., Arifin, A. Z., Sarimuddin, S., Nugroho, A. S., Rozi, I. E. and Asih, P. B. S. (2018). Segmentation of malaria parasite candidate from thickblood smear microscopic images using watershed and adaptive thresholding. Journal of Telecommunication, Electronic and Computer Engineering (JTEC), 10(2-4), 113-117.
  33. Savkare, S. S., Narote, A. S. and Narote, S. P. (2016, September). Automatic blood cell segmentation using K-Mean clustering from microscopic thin blood images. In Proceedings of the Third International Symposium on Computer Vision and the Internet (pp. 8-11).
  34. Nanoti, A., Jain, S., Gupta, C. and Vyas, G. (2016). Detection of malaria parasite species and life cycle stages using microscopic images of thin blood smear. 2016 International Conference on Inventive Computation Technologies (ICICT), Coimbatore, pp. 1-6. doi: 10.1109/INVENTIVE.2016.7823258.
  35. Widiawati, C. R. A., Nugroho, H. A. and Ardiyanto, I. (2016, August). Plasmodium detection methods in thick blood smear images for diagnosing Malaria: A review. In 2016 1st International Conference on Information Technology, Information Systems and Electrical Engineering (ICITISEE) (pp. 142-147). IEEE.
  36. Harris, M., Ku, B., Lim, C. and Ko, H. (2017, January). Automated malaria cell counter using Hough transform based method. In 2017 IEEE International Conference on Consumer Electronics (ICCE) (pp. 404-405). IEEE.
  37. Kudisthalert, W., Pasupa, K. and Tongsima, S. (2020). Counting and Classification of Malarial Parasite From Giemsa-Stained Thin Film Images," in IEEE Access, vol. 8, pp. 78663-78682. doi: 10.1109/ACCESS.2020.2990497.
  38. Mustafa, W. A., Abdul-Nasir, A. S., Mohamed, Z. and Yazid, H. (2018). Segmentation based on morphological approach for enhanced malaria parasites detection. Journal of Telecommunication, Electronic and Computer Engineering (JTEC), 10(1-16), 15-20.
  39. Zhang, Z., Ong, L. S., Fang, K., Matthew, A., Dauwels, J., Dao, M. and Asada, H. (2016, August). Image classification of unlabeled malaria parasites in red blood cells. In 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) (pp. 3981-3984). IEEE.
  40. Altman, D. G. and Bland, J. M. (1994). Statistics Notes: Diagnostic tests 1: sensitivity and specificity. BMJ, 308(6943), 1552–1552.
Index Terms

Computer Science
Information Sciences

Keywords

Algorithm Feature Extraction Image Processing Malaria Malaria Cell Parasite Count Plasmodium Pre-processing Segmentation.

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