Published 2023-12-31
Keywords
- Parasitic Eggs, Deep Learning, CNN, SVM, DNN, YOLO.
How to Cite
C, K., Mishra, K., & Kannan, D. (2023). Detection of Parasitic Eggs Using Deep Learning: A Survey. Kristu Jayanti Journal of Computational Sciences (KJCS), 3(1), 11–22. https://doi.org/10.59176/kjcs.v3i1.2291
Abstract
The prevalence of parasitic infections continues to threaten global public health significantly. Identifying and detecting parasitic eggs in stool samples remain crucial for accurate diagnosis and prompt treatment. Recent advancements in deep learning techniques have opened up new possibilities for the automated detection and classification of parasitic eggs. This survey paper presents a comprehensive overview of the latest research on using deep learning to detect parasitic eggs in stool samples. The paper discusses the challenges associated with traditional methods of egg detection and highlights the various deep-learning models developed to improve diagnostic accuracy. Additionally, the paper provides a thorough breakdown of the present state-of-the-art techniques, identifies gaps in the literature, and suggests potential avenues for a future research. This paper aims to serve as a valuable resource for researchers, clinicians, and public health officials working toward developing accurate, efficient, and cost-effective methods for diagnosing parasitic infections.
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References
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[3] Naing KM, Boonsang S, Chuwongin S, Kittichai V, Tongloy T, Prommongkol S, Dekumyoy P, Watthanakulpanich D. Automatic recognition of parasitic products in stool examination using object detection approach. PeerJ Comput Sci. 2022 Aug 17;8:e1065. doi: 10.7717/peerj-cs.1065. PMID: 36092001; PMCID: PMC9455271.
[4] Osaku D, Cuba CF, Suzuki CTN, Gomes JF, Falcão AX. Automated diagnosis of intestinal parasites: A new hybrid approach and its benefits. Comput Biol Med. 2020 Aug;123:103917. doi: 10.1016/j.compbiomed.2020.103917. Epub 2020 Jul 15. PMID: 32768052.
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[6] Oliveira WJ, Magalhães FDC, Elias AMS, de Castro VN, Favero V, Lindholz CG, Oliveira ÁA, Barbosa FS, Gil F, Gomes MA, Graeff-Teixeira C, Enk MJ, Coelho PMZ, Carneiro M, Negrão-Corrêa DA, Geiger SM. Evaluation of diagnostic methods for the detection of intestinal schistosomiasis in endemic areas with low parasite loads: Saline gradient, Helmintex, Kato-Katz and rapid urine test. PLoS Negl Trop Dis. 2018 Feb 22;12(2):e0006232. doi: 10.1371/journal.pntd.0006232.
[7] Inácio SV, Gomes JF, Falcão AX, Martins Dos Santos B, Soares FA, Nery Loiola SH, Rosa SL, Nagase Suzuki CT, Bresciani KDS. Automated Diagnostics: Advances in the Diagnosis of Intestinal Parasitic Infections in Humans and Animals. Front Vet Sci. 2021 Nov 23;8:715406. doi: 10.3389/fvets.2021.715406. PMID: 34888371; PMCID: PMC8650151.
[8] Kumar, S., Arif, T., Alotaibi, A.S. et al. Advances Towards Automatic Detection and Classification of Parasites Microscopic Images Using Deep Convolutional Neural Network: Methods, Models and Research Directions. Arch Computat Methods Eng 30, 2013–2039 (2023). https://doi.org/10.1007/s11831-022-09858-w
[9] Beaudelaire Saha Tchinda, Michel Noubom, Daniel Tchiotsop, Valerie Louis-Dorr, Didier Wolf, Towards an automated medical diagnosis system for intestinal parasitosis, Informatics in Medicine Unlocked, ISSN 2352-9148, https://doi.org/10.1016/j.imu.2018.09.004.
[10] Nkamgang OT, Tchiotsop D, Fotsin HB, Talla PK (2018) An Expert System for Assistance in Human Intestinal Parasitosis Diagnosis. Biosens Bioelectron Open Acc: BBOA-128. DOI: 10.29011/ 2577-2260.100028
[11] Oscar Takam Nkamgang, Daniel Tchiotsop, Beaudelaire Saha Tchinda, Hilaire Bertrand Fotsin, A neuro-fuzzy system for automated detection and classification of human intestinal parasites, Informatics in Medicine Unlocked, ISSN 2352-9148, https://doi.org/10.1016/j.imu.2018.10.007.
[12] Litjens G, Kooi T, Bejnordi BE, Setio AAA, Ciompi F, Ghafoorian M, van der Laak JAWM, van Ginneken B, Sánchez CI. A survey on deep learning in medical image analysis. Med Image Anal. 2017 Dec;42:60-88. doi: 10.1016/j.media.2017.07.005. Epub 2017 Jul 26. PMID: 28778026.
[13] Lee JG, Jun S, Cho YW, Lee H, Kim GB, Seo JB, Kim N. Deep Learning in Medical Imaging: General Overview. Korean J Radiol. 2017 Jul-Aug;18(4):570-584. doi: 10.3348/kjr.2017.18.4.570. Epub 2017 May 19. PMID: 28670152; PMCID: PMC5447633.
[14] Razzak, Muhammad & Naz, Saeeda & Zaib, Ahmad. (2018). Deep Learning for Medical Image Processing: Overview, Challenges and the Future. 10.1007/978-3-319-65981-7_12.
[15] Han ET, Guk SM, Kim JL, Jeong HJ, Kim SN, Chai JY. Detection of parasite eggs from archaeological excavations in the Republic of Korea. Mem Inst Oswaldo Cruz. 2003;98 Suppl 1:123-6. doi: 10.1590/s0074-02762003000900018. PMID: 12687771.
[16] Maier A, Syben C, Lasser T, Riess C. A gentle introduction to deep learning in medical image processing. Z Med Phys. 2019 May;29(2):86-101. doi: 10.1016/j.zemedi.2018.12.003. Epub 2019 Jan 25. PMID: 30686613.
[17] Doaa El Said Said, Detection of parasites in commonly consumed raw vegetables, Alexandria Journal of Medicine, ISSN 2090-5068, https://doi.org/10.1016/j.ajme.2012.05.005.
[18] Wichmann D, Panning M, Quack T, Kramme S, Burchard GD, Grevelding C, Drosten C. Diagnosing schistosomiasis by detection of cell-free parasite DNA in human plasma. PLoS Negl Trop Dis. 2009;3(4):e422. doi: 10.1371/journal.pntd.0000422. Epub 2009 Apr 21. PMID: 19381285; PMCID: PMC2667260.
[19] Kitvimonrat, Apichon & Hongcharoen, Natthaphon & Marukatat, Sanparith & Watcharabutsarakham, Sarin. (2020). Automatic Detection and Characterization of Parasite Eggs using Deep Learning Methods. 153-156. 10.1109/ECTI-CON49241.2020.9158084.
[20] Ray, Kaushik & Shil, Sukhen & Saharia, Sarat & Sarma, Nityananda & Karabasanavar, Nagappa. (2020). Detection and Identification of Parasite Eggs from Microscopic Images of Fecal Samples. 10.1007/978-981-13-9042-5_5.
[21] Butploy, N., Kanarkard, W., & Maleewong Intapan, P. (2021). Deep Learning Approach for Ascaris lumbricoides Parasite Egg Classification. Journal of Parasitology Research, 2021.
[22] W. Nhidi, N. B. Aoun and R. Ejbali, "Deep Learning-Based Parasitic Egg Identification From a Slender-Billed Gull’s Nest," in IEEE Access, vol. 11, pp. 37194-37202, 2023, doi: 10.1109/ACCESS.2023.3267083.
[23] David Opeoluwa Oyewola, Emmanuel Gbenga Dada, Sanjay Misra & Robertas Damaševičius (2022) A Novel Data Augmentation Convolutional Neural Network for Detecting Malaria Parasite in Blood Smear Images, Applied Artificial Intelligence, 36:1, DOI: 10.1080/08839514.2022.2033473.
[1] Suwannaphong, Thanaphon & Chavana, Sawaphob & Tongsom, Sahapol & Palasuwan, Duangdao & Chalidabhongse, Thanarat & Anantrasirichai, Nantheera. (2021). Parasitic Egg Detection and Classification in Low-cost Microscopic Images using Transfer Learning.
[2] Li, Qiaoliang & Li, Shiyu & Liu, Xinyu & He, Zhuoying & Wang, Tao & Xu, Ying & Guan, Huimin & Chen, Runmin & Qi, Suwen & Wang, Feng. (2020). FecalNet: Automated detection of visible components in human feces using deep learning. Medical Physics. 47. 10.1002/mp.14352.
[3] Naing KM, Boonsang S, Chuwongin S, Kittichai V, Tongloy T, Prommongkol S, Dekumyoy P, Watthanakulpanich D. Automatic recognition of parasitic products in stool examination using object detection approach. PeerJ Comput Sci. 2022 Aug 17;8:e1065. doi: 10.7717/peerj-cs.1065. PMID: 36092001; PMCID: PMC9455271.
[4] Osaku D, Cuba CF, Suzuki CTN, Gomes JF, Falcão AX. Automated diagnosis of intestinal parasites: A new hybrid approach and its benefits. Comput Biol Med. 2020 Aug;123:103917. doi: 10.1016/j.compbiomed.2020.103917. Epub 2020 Jul 15. PMID: 32768052.
[5] Cai, Lei & Gao, Jingyang & Zhao, Di. (2020). A review of the application of deep learning in medical image classification and segmentation. Annals of Translational Medicine. 8. 713-713. 10.21037/atm.2020.02.44.
[6] Oliveira WJ, Magalhães FDC, Elias AMS, de Castro VN, Favero V, Lindholz CG, Oliveira ÁA, Barbosa FS, Gil F, Gomes MA, Graeff-Teixeira C, Enk MJ, Coelho PMZ, Carneiro M, Negrão-Corrêa DA, Geiger SM. Evaluation of diagnostic methods for the detection of intestinal schistosomiasis in endemic areas with low parasite loads: Saline gradient, Helmintex, Kato-Katz and rapid urine test. PLoS Negl Trop Dis. 2018 Feb 22;12(2):e0006232. doi: 10.1371/journal.pntd.0006232.
[7] Inácio SV, Gomes JF, Falcão AX, Martins Dos Santos B, Soares FA, Nery Loiola SH, Rosa SL, Nagase Suzuki CT, Bresciani KDS. Automated Diagnostics: Advances in the Diagnosis of Intestinal Parasitic Infections in Humans and Animals. Front Vet Sci. 2021 Nov 23;8:715406. doi: 10.3389/fvets.2021.715406. PMID: 34888371; PMCID: PMC8650151.
[8] Kumar, S., Arif, T., Alotaibi, A.S. et al. Advances Towards Automatic Detection and Classification of Parasites Microscopic Images Using Deep Convolutional Neural Network: Methods, Models and Research Directions. Arch Computat Methods Eng 30, 2013–2039 (2023). https://doi.org/10.1007/s11831-022-09858-w
[9] Beaudelaire Saha Tchinda, Michel Noubom, Daniel Tchiotsop, Valerie Louis-Dorr, Didier Wolf, Towards an automated medical diagnosis system for intestinal parasitosis, Informatics in Medicine Unlocked, ISSN 2352-9148, https://doi.org/10.1016/j.imu.2018.09.004.
[10] Nkamgang OT, Tchiotsop D, Fotsin HB, Talla PK (2018) An Expert System for Assistance in Human Intestinal Parasitosis Diagnosis. Biosens Bioelectron Open Acc: BBOA-128. DOI: 10.29011/ 2577-2260.100028
[11] Oscar Takam Nkamgang, Daniel Tchiotsop, Beaudelaire Saha Tchinda, Hilaire Bertrand Fotsin, A neuro-fuzzy system for automated detection and classification of human intestinal parasites, Informatics in Medicine Unlocked, ISSN 2352-9148, https://doi.org/10.1016/j.imu.2018.10.007.
[12] Litjens G, Kooi T, Bejnordi BE, Setio AAA, Ciompi F, Ghafoorian M, van der Laak JAWM, van Ginneken B, Sánchez CI. A survey on deep learning in medical image analysis. Med Image Anal. 2017 Dec;42:60-88. doi: 10.1016/j.media.2017.07.005. Epub 2017 Jul 26. PMID: 28778026.
[13] Lee JG, Jun S, Cho YW, Lee H, Kim GB, Seo JB, Kim N. Deep Learning in Medical Imaging: General Overview. Korean J Radiol. 2017 Jul-Aug;18(4):570-584. doi: 10.3348/kjr.2017.18.4.570. Epub 2017 May 19. PMID: 28670152; PMCID: PMC5447633.
[14] Razzak, Muhammad & Naz, Saeeda & Zaib, Ahmad. (2018). Deep Learning for Medical Image Processing: Overview, Challenges and the Future. 10.1007/978-3-319-65981-7_12.
[15] Han ET, Guk SM, Kim JL, Jeong HJ, Kim SN, Chai JY. Detection of parasite eggs from archaeological excavations in the Republic of Korea. Mem Inst Oswaldo Cruz. 2003;98 Suppl 1:123-6. doi: 10.1590/s0074-02762003000900018. PMID: 12687771.
[16] Maier A, Syben C, Lasser T, Riess C. A gentle introduction to deep learning in medical image processing. Z Med Phys. 2019 May;29(2):86-101. doi: 10.1016/j.zemedi.2018.12.003. Epub 2019 Jan 25. PMID: 30686613.
[17] Doaa El Said Said, Detection of parasites in commonly consumed raw vegetables, Alexandria Journal of Medicine, ISSN 2090-5068, https://doi.org/10.1016/j.ajme.2012.05.005.
[18] Wichmann D, Panning M, Quack T, Kramme S, Burchard GD, Grevelding C, Drosten C. Diagnosing schistosomiasis by detection of cell-free parasite DNA in human plasma. PLoS Negl Trop Dis. 2009;3(4):e422. doi: 10.1371/journal.pntd.0000422. Epub 2009 Apr 21. PMID: 19381285; PMCID: PMC2667260.
[19] Kitvimonrat, Apichon & Hongcharoen, Natthaphon & Marukatat, Sanparith & Watcharabutsarakham, Sarin. (2020). Automatic Detection and Characterization of Parasite Eggs using Deep Learning Methods. 153-156. 10.1109/ECTI-CON49241.2020.9158084.
[20] Ray, Kaushik & Shil, Sukhen & Saharia, Sarat & Sarma, Nityananda & Karabasanavar, Nagappa. (2020). Detection and Identification of Parasite Eggs from Microscopic Images of Fecal Samples. 10.1007/978-981-13-9042-5_5.
[21] Butploy, N., Kanarkard, W., & Maleewong Intapan, P. (2021). Deep Learning Approach for Ascaris lumbricoides Parasite Egg Classification. Journal of Parasitology Research, 2021.
[22] W. Nhidi, N. B. Aoun and R. Ejbali, "Deep Learning-Based Parasitic Egg Identification From a Slender-Billed Gull’s Nest," in IEEE Access, vol. 11, pp. 37194-37202, 2023, doi: 10.1109/ACCESS.2023.3267083.
[23] David Opeoluwa Oyewola, Emmanuel Gbenga Dada, Sanjay Misra & Robertas Damaševičius (2022) A Novel Data Augmentation Convolutional Neural Network for Detecting Malaria Parasite in Blood Smear Images, Applied Artificial Intelligence, 36:1, DOI: 10.1080/08839514.2022.2033473.