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

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

April 7, 2023
July 12, 2023
November 20, 2023
egg parasite classification, deep learning, ensemble learning, hard vote, soft vote

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:
  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.
  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.
  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.
  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.
  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.
  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.
  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.
  9. [9] M. Tumusiime et al., “Prevalence of swine gastrointestinal parasites in Nyagatare District, Rwanda,” J. Parasitol. Res., Vol.2020, Article No.8814136, 2020.
  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.
  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.
  12. [12] T. Suwannaphong et al., “Parasitic egg detection and classification in low-cost microscopic images using transfer learning,” arXiv: 2107.00968, 2021.
  13. [13] C. Cao et al., “Deep learning and its applications in biomedicine,” Genom. Proteom. Bioinform., Vol.16, No.1, pp. 17-32, 2018.
  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.
  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.
  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.
  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.
  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.
  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.
  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.
  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.
  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.
  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.
  24. [24] K. Sriporn et al., “Analyzing malaria disease using effective deep learning approach,” Diagnostics, Vol.10, No.10, Article No.744, 2020.
  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.
  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.
  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.
  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.
  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.
  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.
  32. [32] Y. Wang et al., “A robust ensemble model for patasitic egg detection and classification,” arXiv: 2207.01419, 2022.
  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.
  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.
  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.

*This site is desgined based on HTML5 and CSS3 for modern browsers, e.g. Chrome, Firefox, Safari, Edge, Opera.

Last updated on Nov. 24, 2023