Superior Automatic Screening for Human Helminthic Ova by Using Self-supervised Learning Approach-Based Object Classification
| dc.contributor.author | Natchapon Pinetsuksai | |
| dc.contributor.author | Veerayuth Kittichai | |
| dc.contributor.author | Rangsan Jomtarak | |
| dc.contributor.author | Komgrit Jaksukam | |
| dc.contributor.author | Teerawat Tongloy | |
| dc.contributor.author | Siridech Boonsang | |
| dc.contributor.author | Santhad Chuwongin | |
| dc.contributor.correspondence | S. Chuwongin; College of Advanced Manufacturing Innovation, King MongkutÕs Institute of Technology Ladkrabang, Bangkok, Thailand; email: santhad.ch@kmitl.ac.th | |
| dc.date.accessioned | 2025-03-10T07:34:44Z | |
| dc.date.available | 2025-03-10T07:34:44Z | |
| dc.date.issued | 2023 | |
| dc.description.abstract | Human parasitic infections remain one of public health concerns for 1.5 billion people worldwide including Thailand. Conventional microscopic examination is a gold standard method and often used to identify the helminth ova and filariform larvae and also protozoa cyst in stool-dependent simple smear. The benefits of traditional techniques are diminished by time-consuming, complicated procedures, massive labor, and skilled and trained parasitologists. An automatically rapid screening of the most in need of treatment is considered to replace the conventional technique. Here, we aim to develop a deep convolutional residual network based self-supervised learning model to identify mostly common parasite ova in Thailand. Although small amounts of training data was used to train the proposed model, the result shows superior performance over 95% accuracy. As a result, low values of false positive and false negative based confusion matrix table found revealed the robustness of the proposed models. General accuracy of self-supervised learning based the area under a ROC curve proposed with greater than 94% is also support an outstanding model studied. Therefore, rank of 1% to 10% of fine-tuning data labelled used bring us about a comparable model to that of using a 100% labelled training data. These findings emphasize the transformative potential of the BYOL method for screening of parasitic infection, particularly in resource-limited settings where is a lack of supportive lab equipment and skilled parasitologists to manage a large amount of challenging data in the future. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG. | |
| dc.identifier.citation | Communications in Computer and Information Science | |
| dc.identifier.doi | 10.1007/978-3-031-42430-4_4 | |
| dc.identifier.isbn | 978-303142429-8 | |
| dc.identifier.issn | 18650929 | |
| dc.identifier.scopus | 2-s2.0-85174551746 | |
| dc.identifier.uri | https://repository.dusit.ac.th//handle/123456789/4530 | |
| dc.language | English | |
| dc.publisher | Springer Science and Business Media Deutschland GmbH | |
| dc.rights.holder | Scopus | |
| dc.subject | Bootstrap Your Own Latent (BYOL) | |
| dc.subject | Helminthic eggs | |
| dc.subject | Object classification | |
| dc.subject | Self-supervised learning | |
| dc.subject | Similarity loss | |
| dc.title | Superior Automatic Screening for Human Helminthic Ova by Using Self-supervised Learning Approach-Based Object Classification | |
| dc.type | Conference paper | |
| mods.location.url | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85174551746&doi=10.1007%2f978-3-031-42430-4_4&partnerID=40&md5=2391e688840200272114f68f3a0237aa | |
| oaire.citation.endPage | 51 | |
| oaire.citation.startPage | 40 | |
| oaire.citation.volume | 1863 CCIS |