Efficient Classification Techniques in Classifying Human Intestinal Parasite Ova
Keywords:
Helminth, Parasite ova, k-NN, SVM, EnsembleAbstract
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
How to Cite
Issue
Section
License
TRANSFER OF COPYRIGHT AGREEMENT
The manuscript is herewith submitted for publication in the Journal of Telecommunication, Electronic and Computer Engineering (JTEC). It has not been published before, and it is not under consideration for publication in any other journals. It contains no material that is scandalous, obscene, libelous or otherwise contrary to law. When the manuscript is accepted for publication, I, as the author, hereby agree to transfer to JTEC, all rights including those pertaining to electronic forms and transmissions, under existing copyright laws, except for the following, which the author(s) specifically retain(s):
- All proprietary right other than copyright, such as patent rights
- The right to make further copies of all or part of the published article for my use in classroom teaching
- The right to reuse all or part of this manuscript in a compilation of my own works or in a textbook of which I am the author; and
- The right to make copies of the published work for internal distribution within the institution that employs me
I agree that copies made under these circumstances will continue to carry the copyright notice that appears in the original published work. I agree to inform my co-authors, if any, of the above terms. I certify that I have obtained written permission for the use of text, tables, and/or illustrations from any copyrighted source(s), and I agree to supply such written permission(s) to JTEC upon request.