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JACIII Vol.27 No.6 pp. 1113-1121
doi: 10.20965/jaciii.2023.p1113
(2023)

Research Paper:

An Approach for Egg Parasite Classification Based on Ensemble Deep Learning

Narut Butploy* ORCID Icon, Wanida Kanarkard*,† ORCID Icon, Pewpan M. Intapan** ORCID Icon, and Oranuch Sanpool** ORCID Icon

*Department of Computer Engineering, Khon Kaen University
123 Moo 16 Mittraphap Road, Nai-Muang, Muang District, Khon Kaen 40002, Thailand

Corresponding author

**Faculty of Medicine, Khon Kaen University
123 Moo 16 Mittraphap Road, Nai-Muang, Muang District, Khon Kaen 40002, Thailand

Received:
April 7, 2023
Accepted:
July 12, 2023
Published:
November 20, 2023
Keywords:
egg parasite classification, deep learning, ensemble learning, hard vote, soft vote
Abstract

Opisthorchis viverrini and minute intestinal fluke (MIF) infections are heavily epidemic in northeastern Thailand. Their primary cause is eating raw or undercooked cyprinid fishes, and they cause health problems in the human digestive system. In cases of liver fluke, these parasites can go through the bile duct system, which may cause cholangiocarcinoma (bile duct cancer). When a medical doctor suspects that a patient is infected with parasites, they typically request a stool analysis to determine the type of egg parasites using microscopy. Both parasites have similar characteristics, thus, it is necessary for a specialist to identify the specific type of egg parasites present. Many automatic systems have been developed using deep learning to assist doctors in diagnosing the type of egg parasite. In this study, we proposed three models of deep learning architectures and created voting ensembles to analyze egg parasite images. Images of similar liver fluke eggs and MIF eggs were taken from the Parasitology Laboratory, Faculty of Medicine, Khon Kaen University, Khon Kaen, Thailand. Image data augmentation is used to expand images from different perspectives and assist the system in acquiring a greater variety of images. Three models performed effectively, by employing the hard voting ensemble, the accuracy increased to 86.67%, while for the second group, the accuracies reached 68.00%, 76.00%, and 77.33%, respectively. Using the soft voting ensemble, the accuracy improved to 79.33%. These outcomes highlight the potential of ensemble deep learning in image classification. Furthermore, these results align closely with those achieved by several experts in image classification. Hence, a promising ensemble approach can aid doctors in accurately classifying images of egg parasites.

Cite this article as:
N. Butploy, W. Kanarkard, P. Intapan, and O. Sanpool, “An Approach for Egg Parasite Classification Based on Ensemble Deep Learning,” J. Adv. Comput. Intell. Intell. Inform., Vol.27 No.6, pp. 1113-1121, 2023.
Data files:
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Last updated on Jul. 19, 2024