Jearanaitanakij, Kietikul

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Jearanaitanakij, Kietikul
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kietikul.je@kmitl.ac.th

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    Item type:Publication,
    Compact Thai Sign Language Translation by Deep Learning
    (2024-01-01)
    Jearanaitanakij, Kietikul
    ;
    Choojan, Piyathida
    ;
    Thongtem, Piyada
    Sign language translation is a challenging problem in natural language processing. Its principle involves machine translation from sign language images to spoken language text. Designing a good translation is not a trivial task since there are a large number of both input image pixels and output classes. We propose the deep learning model to translate static gestures of Thai sign language (TSL) to the corresponding Thai spoken words. The main objective is to design a compact model that delivers high performance so that it can be implemented on mobile devices. Several mobile convolutional neural networks (CNN) are investigated to find the best backbone architecture. We also attach additional layers to the selected CNN architecture to fine-tune its performance. The experiments on the dataset collected from twenty-four volunteers indicate excellent results; in terms of precision, recall, and f1-score, of the proposed model. The comparisons with the state-of-the-art models and the feature visualizations from convolution layers endorse its effectiveness.
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    DEEP LEARNING DEVELOPMENTAL MODEL FOR EMBRYO GRADING IN THAI POPULATION
    (2024-01-01)
    Wiriyasirivaj, Budsaba
    ;
    Limkiatsataporn, Sawit
    ;
    Pukinghin, Apisit
    ;
    Kuekulkomain, Phatrapron
    ;
    Promrungrueng, Pornprom
    In 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.