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.

1. [1] B. Sripa et al., “Current status of human liver fluke infections in the Greater Mekong Subregion,” Acta Trop., Vol.224, Article No.106133, 2021. https://doi.org/10.1016/j.actatropica.2021.106133
2. [2] O. Sanpool et al., “Human liver fluke Opisthorchis viverrini (Trematoda, Opisthorchiidae) in Central Myanmar: New records of adults and metacercariae identified by morphology and molecular analysis,” Acta Trop., Vol.185, pp. 149-155, 2018. https://doi.org/10.1016/j.actatropica.2018.05.009
3. [3] P. M. Intapan et al., “Immunodiagnosis of human fascioliasis using an antigen of Fasciola gigantica adult worm with the molecular mass of 27 kDa by a dot-ELISA,” Southeast Asian J. Trop. Med. Public Health., Vol.34, No.4, pp. 713-717, 2003.
4. [4] A.-H. S. Lukambagire, D. N. Mchaile, and M. Nyindo, “Diagnosis of human fascioliasis in Arusha region, northern Tanzania by microscopy and clinical manifestations in patients,” BMC Infect. Dis., Vol.15, Article No.578, 2015. https://doi.org/10.1186/s12879-015-1326-9
5. [5] A. Wannasan et al., “Identification of Fasciola species based on mitochondrial and nuclear DNA reveals the co-existence of intermediate Fasciola and Fasciola gigantica in Thailand,” Exp. Parasitol., Vol.146, pp. 64-70, 2014. https://doi.org/10.1016/j.exppara.2014.09.006
6. [6] X.-Q. Cai et al., “Rapid detection and differentiation of Clonorchis sinensis and Opisthorchis viverrini using real-time PCR and high resolution melting analysis,” Sci. World J., Vol.2014, Article No.893981, 2014. https://doi.org/10.1155/2014/893981
7. [7] Y.-H. Cao et al., “Rare cause of appendicitis: Mechanical obstruction due to Fasciolopsis buski infestation,” World J. Gastroenterol., Vol.21, No.10, pp. 3146-3149, 2015. https://doi.org/10.3748/wjg.v21.i10.3146
8. [8] J. Ma et al., “Fasciolopsis buski (Digenea: Fasciolidae) from China and India may represent distinct taxa based on mitochondrial and nuclear ribosomal DNA sequences,” Parasites Vectors, Vol.10, Article No.101, 2017. https://doi.org/10.1186/s13071-017-2039-2
9. [9] M. Tumusiime et al., “Prevalence of swine gastrointestinal parasites in Nyagatare District, Rwanda,” J. Parasitol. Res., Vol.2020, Article No.8814136, 2020. https://doi.org/10.1155/2020/8814136
10. [10] O. Chuchuen et al., “Rapid label-free analysis of Opisthorchis viverrini eggs in fecal specimens using confocal Raman spectroscopy,” PLOS ONE, Vol.14, No.12, Article No.e0226762, 2019. https://doi.org/10.1371/journal.pone.0226762
11. [11] W. Kaewkong et al., “Molecular differentiation of Opisthorchis viverrini and Clonorchis sinensis eggs by multiplex real-time PCR with high resolution melting analysis,” Korean J. Parasito., Vol.51, No.6, pp. 689-694, 2013. https://doi.org/10.3347/kjp.2013.51.6.689
12. [12] T. Suwannaphong et al., “Parasitic egg detection and classification in low-cost microscopic images using transfer learning,” arXiv: 2107.00968, 2021. https://doi.org/10.48550/arXiv.2107.00968
13. [13] C. Cao et al., “Deep learning and its applications in biomedicine,” Genom. Proteom. Bioinform., Vol.16, No.1, pp. 17-32, 2018. https://doi.org/10.1016/j.gpb.2017.07.003
14. [14] O. T. Nkamgang et al., “A neuro-fuzzy system for automated detection and classification of human intestinal parasites,” Inform. Med. Unlocked, Vol.13, pp. 81-91, 2018. https://doi.org/10.1016/j.imu.2018.10.007
15. [15] M. Zare et al., “A machine learning-based system for detecting leishmaniasis in microscopic images,” BMC Infect. Dis., Vol.22, Article No.48, 2022. https://doi.org/10.1186/s12879-022-07029-7
16. [16] S. Rajaraman et al., “Understanding the learned behavior of customized convolutional neural networks toward malaria parasite detection in thin blood smear images,” J. Med. Imaging, Vol.5, No.3, Article No.034501, 2018. https://doi.org/10.1117/1.JMI.5.3.034501
17. [17] C. A. Takam et al., “Spark architecture for deep learning-based dose optimization in medical imaging,” Inform. Med. Unlocked, Vol.19, Article No.100335, 2020. https://doi.org/10.1016/j.imu.2020.100335
18. [18] N. Butploy, W. Kanarkard, and P. M. Intapan, “Deep learning approach for Ascaris lumbricoides parasite egg classification,” J. Parasitol. Res., Vol.2021, Article No.6648038, 2021. https://doi.org/10.1155/2021/6648038
19. [19] P. Saha, M. S. Sadi, and M. M. Islam, “EMCNet: Automated COVID-19 diagnosis from X-ray images using convolutional neural network and ensemble of machine learning classifiers,” Inform. Med. Unlocked, Vol.22, Article No.100505, 2021. https://doi.org/10.1016/j.imu.2020.100505
20. [20] J. Liu and Y. Li, “Visual servoing with deep learning and data augmentation for robotic manipulation,” J. Adv. Comput. Intell. Intell. Inform., Vol.24, No.7, pp. 953-962, 2020. https://doi.org/10.20965/jaciii.2020.p0953
21. [21] C. Shorten and T. M. Khoshgoftaar, “A survey on image data augmentation for deep learning,” J. Big Data, Vol.6, Article No.60, 2019. https://doi.org/10.1186/s40537-019-0197-0
22. [22] Y. Dan and Z. Li, “Particle swarm optimization-based convolutional neural network for handwritten Chinese character recognition,” J. Adv. Comput. Intell. Intell. Inform., Vol.27, No.2, pp. 165-172, 2023. https://doi.org/10.20965/jaciii.2023.p0165
23. [23] K. M. F. Fuhad et al., “Deep learning based automatic malaria parasite detection from blood smear and its smartphone based application,” Diagnostics, Vol.10, No.5, Article No.329, 2020. https://doi.org/10.3390/diagnostics10050329
24. [24] K. Sriporn et al., “Analyzing malaria disease using effective deep learning approach,” Diagnostics, Vol.10, No.10, Article No.744, 2020. https://doi.org/10.3390/diagnostics10100744
25. [25] H. E. Osman, “Variable ranking for online ensemble learning,” J. Adv. Comput. Intell. Intell. Inform., Vol.13, No.3, pp. 331-337, 2009. https://doi.org/10.20965/jaciii.2009.p0331
26. [26] R. J. Oidtman et al., “Trade-offs between individual and ensemble forecasts of an emerging infectious disease,” Nat. Commun., Vol.12, Article No.5379, 2021. https://doi.org/10.1038/s41467-021-25695-0
27. [27] Z.-H. Zhou, “Ensemble Methods: Foundations and Algorithms,” Chapman & Hall, 2012.
28. [28] Y. Zhou et al., “Extracting salient features from convolutional discriminative filters,” Inf. Sci., Vol.558, pp. 265-279, 2021. https://doi.org/10.1016/j.ins.2020.12.084
29. [29] S. Y. Kim and A. Upneja, “Majority voting ensemble with a decision trees for business failure prediction during economic downturns,” J. Innov. Knowl., Vol.6, No.2, pp. 112-123, 2021. https://doi.org/10.1016/j.jik.2021.01.001
30. [30] L. Wang et al., “A heterogeneous ensemble learning voting method for fatigue detection in daily activities,” J. Adv. Comput. Intell. Intell. Inform., Vol.22, No.1, pp. 88-96, 2018. https://doi.org/10.20965/jaciii.2018.p0088
31. [31] A. M. Pirbazari et al., “An ensemble approach for multi-step ahead energy forecasting of household communities,” IEEE Access, Vol.9, pp. 36218-36240, 2021. https://doi.org/10.1109/ACCESS.2021.3063066
32. [32] Y. Wang et al., “A robust ensemble model for patasitic egg detection and classification,” arXiv: 2207.01419, 2022. https://doi.org/10.48550/arXiv.2207.01419
33. [33] M. Bhuiyan and M. S. Islam, “A new ensemble learning approach to detect malaria from microscopic red blood cell images,” Sens. Int., Vol.4, Article No.100209, 2023. https://doi.org/10.1016/j.sintl.2022.100209
34. [34] M. Sabzevari, G. Martínez-Muñoz, and A. Suárez, “Building heterogeneous ensembles by pooling homogeneous ensembles,” Int. J. Mach. Learn Cybern., Vol.13, No.2, pp. 551-558, 2022. https://doi.org/10.1007/s13042-021-01442-1
35. [35] G. Kyriakides and K. G. Margaritis, “Hands-On Ensemble Learning with Python: Build Highly Optimized Ensemble Machine Learning Models Using Scikit-Learn and Keras,” Packt Publishing, 2019.