Efficient Classification Techniques in Classifying Human Intestinal Parasite Ova

Authors

  • N.A.A Khairudin Faculty of Electrical Engineering Technology, Universiti Malaysia Perlis, UniCITI Alam Campus, Sungai Chuchuh, 02100 Padang Besar, Perlis, Malaysia
  • C.C Lim Faculty of Electronic Engineering Technology, University Malaysia Perlis, Pauh Putra Campus, 02600 Arau, Perlis, Malaysia.
  • A.S.A Nasir Faculty of Electrical Engineering Technology, Universiti Malaysia Perlis, UniCITI Alam Campus, Sungai Chuchuh, 02100 Padang Besar, Perlis, Malaysia
  • Z. Mohamed Department of Microbiology & Parasitology, School of Medical Sciences, Health Campus, Universiti Sains Malaysia, 16150 Kubang Kerian, Kelantan, Malaysia

Keywords:

Helminth, Parasite ova, k-NN, SVM, Ensemble

Abstract

Helminth parasites live in the human body and can cause serious health problems that will lead to cancer and may cause death in patients. These parasitic helminths congregate in the intestines to mate and produce ova. Therefore, early identification screening is necessary to prevent the spread of helminth parasites throughout the body. A manual microscopic feces test is still the most often used approach for helminth detection. As a result, the purpose of this research is to investigate the effectiveness of three classifiers in classifying four types of human intestinal parasite ova. Three classifier techniques used are k-Nearest Neighbourhood (k-NN), Support Vector Machine (SVM), and Ensemble classifier. There are four types of helminth ova which are Ascaris Lumbricoides Ova (ALO), Enterobius Vermicularis Ova (EVO), Hookworm Ova (HWO), and Trichuris Trichiura Ova (TTO). A total of 664 helminth parasite ova images were analyzed, consisting of 166 images from each helminth species. The Linear kernel function from the SVM classifier has obtained the highest accuracy performance reaching 92.23%. Followed by Cityblock distance from the k-NN classifier with an accuracy of 91.16% and AdaBoostM2 from the Ensemble classifier with an accuracy of 89.94%.

References

Y. S. Yang, K. P. Duck, C. K. Hee, M. H. Choi, J. Y. Chai, “Automatic identification of human helminth eggs on microscopic fecal specimens using digital image processing and an artificial neural network,” IEEE Trans. Biomed. Eng. 2001, vol. 48(6), pp. 718–730.

I. D. Amoah, G. Singh, T. A. Stenström, P. Reddy, “Detection and quantification of soil-transmitted helminths in environmental samples: A review of current state-of-the-art and future perspectives,” Acta Trop. 2017, vol. 169, no. February, pp. 187–201.

N. A. A. Khairudin, N. S. Rohaizad, A. S. A. Nasir, L. C. Chin, H. Jaafar, Z. Mohamed, “Image segmentation using k-means clustering and otsu’s thresholding with classification method for human intestinal parasites,” IOP Conf. Ser. Mater. Sci. Eng. 2020, vol. 864, no. 1.

L. B. Huat, A. K. Mitra, J. N. I. Noor, P. C. Dam, M. H. J. Jan, M. W. A. M. Wan, “Prevalence and risk factors of intestinal helminth infection among rural Malay children,” J. Glob. Infect. Dis. 2012, vol. 4, no. 1, pp. 10–14.

K. H. Ghazali, R. S. Hadi, M. Zeehaida, “Microscopy image processing analaysis for automatic detection of human intestinal parasites ALO and TTO,” Int. Conf. Electron. Comput. Comput. ICECCO 2013, 2013, pp. 40–43.

WHO, “Training manual on diagnosis of intestinal parasites : tutor’s guide [electronic resource],” 2004, pp. 1 CD-ROM.

N. Mohd-Shaharuddin, Y. A. L. Lim, N. A. Hassan, S. Nathan, R. Ngui, “Soil-transmitted helminthiasis among indigenous communities in Malaysia: Is this the endless malady with no solution?,” Trop. Biomed., 2018, vol. 35, no. 1, pp. 168–180.

N. A. A. Khairudin, A. S. A. Nasir, L. C. Chin, Z. Mohamed, C. Y. Fook, “An Improvement for Human Intestinal Parasites Detection Methodology using k-Means and Fast k-Means Clustering,” Proc. - 2020 IEEE EMBS Conf. Biomed. Eng. Sci. IECBES 2020, 2021, no. March, pp. 378–383.

R. S. Hadi, K. H. Ghazali, I. Z. Khalidin, M. Zeehaida, “Human parasitic worm detection using image processing technique,” ISCAIE 2012 - 2012 IEEE Symp. Comput. Appl. Ind. Electron., 2012, pp. 196–201.

S. Gokhan, “Biomedical research,” Aging Res. - Methodol. Issues, 2015, vol. 27, no. 3, pp. 27–38.

B. Jimenez, C. Maya, G. Velasquez, F. Torner, F. Arambula, J.A. Barrios, “Identification and quantification of pathogenic helminth eggs using a digital image system,” Exp. Parasitol., 2016, vol. 166, pp. 164–172.

D. Avci, A. Varol, “An expert diagnosis system for classification of human parasite eggs based on multi-class SVM,” Expert Syst. Appl., 2009, vol. 36, no. 1, pp. 43–48.

M. H. Motlagh, “Automatic Segmentation and Classification of Red and White Blood Cells in Thin Blood,” Concordia University, 2015, no. August.

M. Habibzadeh, A. Krzyzak, T. Fevens, A. Sadr, “Counting of RBCs and WBCs in noisy normal blood smear microscopic images,” Med. Imaging 2011 Comput. Diagnosis, 2011, vol. 7963, no. September, p. 79633I.

A. S. Abdul-Nasir, M. Y. Mashor, Z. Mohamed, “Modified global and modified linear contrast stretching algorithms: New colour contrast enhancement techniques for microscopic analysis of malaria slide images,” Comput. Math. Methods Med., 2012, vol. 2012, no. June.

L. C. Chin, N. A. A. Khairudin, S. W. Loke, A. S. A. Nasir, C. Y. Fook, Z. Mohamed, "Comparison of Human Intestinal Parasite Ova Segmentation Using Machine Learning and Deep Learning Techniques" Applied Sciences, 2022, vol 12, no 15, pp 7542. https://doi.org/10.3390/app12157542

M. E. Latoschik, “Realtime 3D Computer Graphics / Virtual Reality-WS: Color Models (p. 13),” Retrieved from https://www.techfak.unibielefeld.de/ags/wbski/lehre/digiSA/WS0607/3DVRCG/Vorlesung/8a.RT3DCGVR-color.pdf

A. E. Hassanien, “Fuzzy rough sets hybrid scheme for breast cancer detection,” Image Vis. Comput., 2007, vol. 25, no. 2, pp. 172–183.

M. Arafah, Q. A. Moghli, “Efficient Image Recognition Technique Using Invariant Moments and Principle Component Analysis,” J. Data Anal. Inf. Process., 2017, vol. 05, no. 01, pp. 1–10.

E. G. Karakasis, A. Amanatiadis, A. Gasteratos, S. A. Chatzichristofis, “Image moment invariants as local features for content based image retrieval using the Bag-of-Visual-Words model,” Pattern Recognit. Lett., 2015, vol. 55, pp. 22–27.

J. M.; Patel, N. C. A. Gamit, “review on feature extraction techniques in Content Based Image Retrieval,” Proc. 2016 IEEE Int. Conf. Wirel. Commun. Signal Process. Networking, WiSPNET 2016, 2016, pp. 2259–2263.

P. Shan, “Image segmentation method based on K-mean algorithm,” Eurasip J. Image Video Process., 2018, vol. 2018, no. 1.

S.; Shafique, S. Tehsin, “Computer-Aided Diagnosis of Acute Lymphoblastic Leukaemia,” Comput. Math. Methods Med., 2018, vol. 2018.

M. M. D. Joshi, P. A. H. Karode, P. S. R. Suralkar, “White Blood Cells Segmentation and Classification to Detect Acute Leukemia,” 2013, vol. 2, no. 3, pp. 147–151.

C. Cortes, V. Vapnik, “Support-Vector Networks,” Machine Learning, 1995, vol. 20, pp. 273-297.

M. R. Sumathi, B. Poorna, “Design and development of ensemble of naïve bayes classifiers to predict social and communication deficiency among children,” Int. J. Appl. Eng. Res., 2017, vol. 12, no. 24, pp. 14190–14198.

L. C. Chin, C. Y. Fook, A. S. A. Nasir, S. N. Basah, M. Y.; Din, Z. Zainuddin, “Classification of the Severity Level for Lower Limb Joint Injuries,” 2020 IEEE-EMBS Conference on Biomedical Engineering and Sciences (IECBES), 2021, pp. 267-270.

Downloads

Published

2022-09-30

How to Cite

Khairudin, N., Lim, C., Nasir, A., & Mohamed, Z. . (2022). Efficient Classification Techniques in Classifying Human Intestinal Parasite Ova. Journal of Telecommunication, Electronic and Computer Engineering (JTEC), 14(3), 17–23. Retrieved from https://jtec.utem.edu.my/jtec/article/view/6199