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Primary Mobile Image Analysis of Human Intestinal Worm Detection

Primary Mobile Image Analysis of Human Intestinal Worm Detection

Justice Kwame Appati, Winfred Yaokumah, Ebenezer Owusu, Paul Nii Tackie Ammah
Copyright: © 2022 |Volume: 11 |Issue: 1 |Pages: 16
ISSN: 2160-9772|EISSN: 2160-9799|EISBN13: 9781683182702|DOI: 10.4018/IJSDA.302631
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MLA

Appati, Justice Kwame, et al. "Primary Mobile Image Analysis of Human Intestinal Worm Detection." IJSDA vol.11, no.1 2022: pp.1-16. http://doi.org/10.4018/IJSDA.302631

APA

Appati, J. K., Yaokumah, W., Owusu, E., & Ammah, P. N. (2022). Primary Mobile Image Analysis of Human Intestinal Worm Detection. International Journal of System Dynamics Applications (IJSDA), 11(1), 1-16. http://doi.org/10.4018/IJSDA.302631

Chicago

Appati, Justice Kwame, et al. "Primary Mobile Image Analysis of Human Intestinal Worm Detection," International Journal of System Dynamics Applications (IJSDA) 11, no.1: 1-16. http://doi.org/10.4018/IJSDA.302631

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Abstract

One among a lot of public health concerns in rural and tropical areas is the human intestinal parasite. Traditionally, diagnosis of these parasites is by visual analysis of stool specimens, which is usually tedious and time-consuming. In this study, the authors combine techniques in the Laplacian pyramid, Gabor filter, and wavelet to build a feature vector for the discrimination of intestinal worm in a low-resolution image captured with mobile devices. The dimension of the feature vector is reduced using principal component analysis, and the resultant vector is considered as input to the SVM classifier. The proposed framework was applied to the Makerere intestinal dataset. At its preliminary stage, the results demonstrate satisfactory classification with an accuracy rate of 65.22% with possible extension in future work.