DEEP LEARNING DEVELOPMENTAL MODEL FOR EMBRYO GRADING IN THAI POPULATION

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dc.contributor.authorWiriyasirivaj, Budsaba
dc.contributor.authorLimkiatsataporn, Sawit
dc.contributor.authorPukinghin, Apisit
dc.contributor.authorKuekulkomain, Phatrapron
dc.contributor.authorPromrungrueng, Pornprom
dc.contributor.authorSupawattanabodee, Busaba
dc.contributor.authorJearanaitanakij, Kietikul
dc.date.accessioned2026-08-06T10:43:48Z
dc.date.available2026-08-06T10:43:48Z
dc.date.issued2024-01-01
dc.description.abstractIn light of the growing challenges associated with infertility, an increasing number of researchers are resorting to assisted reproductive technologies such as In Vitro Fertilization (IVF). Embryo grading is a crucial step in the IVF process that requires embryologists’ expertise. However, their limited availability has led to the exploration of technological alternatives. This study aims to use deep learning for human embryo grading, with models specifically designed for the dataset in Thailand at Vajira Hospital. The process of IVF at Vajira Hospital presents its own set of challenges since its embryo classification extends beyond the Istanbul consensus. Furthermore, classes that occur infrequently are removed and classes with similarities are merged. We apply transfer learning to pretrained deep learning models like VGG19, VGG16, ResNet152, Resnet101, Xception, InceptionV3, and EfficientNet, to create a system capable of accurately classifying embryo quality scores. Experimental results from the embryo dataset collected from Vajira Hospital in Thailand demonstrate the proposed classifier’s accuracy, precision, recall, f1-score, and AUC superiority. This research contributes to the field of IVF in Thailand by potentially reducing human errors and addressing the demand for skilled embryologists.
dc.identifier.citationSuranaree Journal of Science and Technology, 31(4), 1-12, 2024
dc.identifier.doi10.55766/sujst-2024-04-e05589
dc.identifier.issn0858849X
dc.identifier.other2-s2.0-85206323992
dc.identifier.urihttps://dspace.kmitl.ac.th/handle/123456789/15037
dc.sourceSuranaree Journal of Science and Technology
dc.subjectClassification
dc.subjectDeep Learning
dc.subjectEmbryo Grading
dc.subjectIn Vitro Fertilization
dc.subjectInfertility
dc.titleDEEP LEARNING DEVELOPMENTAL MODEL FOR EMBRYO GRADING IN THAI POPULATION
dc.typeArticle

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