Deep Learning Approach for Ascaris lumbricoides Parasite Egg Classification
A. lumbricoides infection affects up to 1/3 of the world population (approximately 1.4 billion people worldwide). It has been estimated that 1.5 billion cases of infection globally and 65,000 deaths occur due to A. lumbricoides. Generally, allied health classifies parasite egg type by using on micro...
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| Format: | Article |
| Language: | English |
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Wiley
2021-01-01
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| Series: | Journal of Parasitology Research |
| Online Access: | http://dx.doi.org/10.1155/2021/6648038 |
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| author | Narut Butploy Wanida Kanarkard Pewpan Maleewong Intapan |
| author_facet | Narut Butploy Wanida Kanarkard Pewpan Maleewong Intapan |
| author_sort | Narut Butploy |
| collection | DOAJ |
| description | A. lumbricoides infection affects up to 1/3 of the world population (approximately 1.4 billion people worldwide). It has been estimated that 1.5 billion cases of infection globally and 65,000 deaths occur due to A. lumbricoides. Generally, allied health classifies parasite egg type by using on microscopy-based methods that are laborious, are limited by low sensitivity, and require high expertise. However, misclassification may occur due to their heterogeneous experience. For their reason, computer technology is considered to aid humans. With the benefit of speed and ability of computer technology, image recognition is adopted to recognize images much more quickly and precisely than human beings. This research proposes deep learning for A. lumbricoides’s egg image recognition to be used as a prototype tool for parasite egg detection in medical diagnosis. The challenge is to recognize 3 types of eggs of A. lumbricoides with the optimal architecture of deep learning. The results showed that the classification accuracy of the parasite eggs is up to 93.33%. This great effectiveness of the proposed model could help reduce the time-consuming image classification of parasite egg. |
| format | Article |
| id | doaj-art-12b8415dc5de4653b3b3c51e791bdcab |
| institution | Kabale University |
| issn | 2090-0023 2090-0031 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Parasitology Research |
| spelling | doaj-art-12b8415dc5de4653b3b3c51e791bdcab2025-02-03T01:06:16ZengWileyJournal of Parasitology Research2090-00232090-00312021-01-01202110.1155/2021/66480386648038Deep Learning Approach for Ascaris lumbricoides Parasite Egg ClassificationNarut Butploy0Wanida Kanarkard1Pewpan Maleewong Intapan2Dept. of Computer Engineering, Khon Kaen University, Khon Kaen 40002, ThailandDept. of Computer Engineering, Khon Kaen University, Khon Kaen 40002, ThailandDept. of Parasitology, Khon Kaen University, Khon Kaen 40002, ThailandA. lumbricoides infection affects up to 1/3 of the world population (approximately 1.4 billion people worldwide). It has been estimated that 1.5 billion cases of infection globally and 65,000 deaths occur due to A. lumbricoides. Generally, allied health classifies parasite egg type by using on microscopy-based methods that are laborious, are limited by low sensitivity, and require high expertise. However, misclassification may occur due to their heterogeneous experience. For their reason, computer technology is considered to aid humans. With the benefit of speed and ability of computer technology, image recognition is adopted to recognize images much more quickly and precisely than human beings. This research proposes deep learning for A. lumbricoides’s egg image recognition to be used as a prototype tool for parasite egg detection in medical diagnosis. The challenge is to recognize 3 types of eggs of A. lumbricoides with the optimal architecture of deep learning. The results showed that the classification accuracy of the parasite eggs is up to 93.33%. This great effectiveness of the proposed model could help reduce the time-consuming image classification of parasite egg.http://dx.doi.org/10.1155/2021/6648038 |
| spellingShingle | Narut Butploy Wanida Kanarkard Pewpan Maleewong Intapan Deep Learning Approach for Ascaris lumbricoides Parasite Egg Classification Journal of Parasitology Research |
| title | Deep Learning Approach for Ascaris lumbricoides Parasite Egg Classification |
| title_full | Deep Learning Approach for Ascaris lumbricoides Parasite Egg Classification |
| title_fullStr | Deep Learning Approach for Ascaris lumbricoides Parasite Egg Classification |
| title_full_unstemmed | Deep Learning Approach for Ascaris lumbricoides Parasite Egg Classification |
| title_short | Deep Learning Approach for Ascaris lumbricoides Parasite Egg Classification |
| title_sort | deep learning approach for ascaris lumbricoides parasite egg classification |
| url | http://dx.doi.org/10.1155/2021/6648038 |
| work_keys_str_mv | AT narutbutploy deeplearningapproachforascarislumbricoidesparasiteeggclassification AT wanidakanarkard deeplearningapproachforascarislumbricoidesparasiteeggclassification AT pewpanmaleewongintapan deeplearningapproachforascarislumbricoidesparasiteeggclassification |