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Author: search.filters.author.Poomrittigul, SuvitSubject: search.filters.subject.Convolutional Neural Network (CNN)

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    Deep Learning to Classify Bacterial Species in the same Genus
    (2024-01-01)
    Sheela, Sherin
    ;
    Piang, May Phu
    ;
    Sakorntanant, Sakda
    ;
    Poomrittigul, Suvit
    ;
    Treebupachatsakul, Treesukon
    Bacterial strains in the same genus share highly similar morphology, gram-staining characteristics, colony sizes, and spatial arrangements. Therefore, identifying them by deep learning can be quite challenging. This study aimed to assess the classification of 7 species of bacteria from 2 genera of Bacillus and Vibrio by using 8 Convolutional Neural Network (CNN) models. We implemented Python programming along with Keras API within the Jupyter Notebook. The models were constructed and evaluated under unbalanced and balanced datasets by augmentation (rotation, flip, etc.). Transfer learning with fine-tuning, and pre-processing of mixup and label smoothing were also applied to reduce overfitting and enhance generalization. Based on the experimental results on private dataset, the results of InceptionResNetV2 emerged as the top-performing model with a notable accuracy of 82.8%, 88.6% precision, 78.4% recall, and 78.0% F1-score when label smoothing was applied at 0.5 on balanced dataset.
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    Classification of Depression Audio Data by Deep Learning
    (2022-01-01)
    Homsiang, Phanomkorn
    ;
    Treebupachatsakul, Treesukon
    ;
    Kiatrungrit, Komsan
    ;
    Poomrittigul, Suvit
    Due to many factors such as anxiety from contracting the disease and concern about the socioeconomic impacts, Thai people have accumulated stress and are at risk of depression. The diagnosis of depression can be primarily assessed by testing the assessments such as PHQ8, PHQ-9, and CES-D. The applied deep learning technology in medicine has received research interest and has been developing. In this research, we tried the classification of depression and non-depression audio datasets with the implementation of 4 model architectures: 1D CNN, 2D CNN, LSTM, and GRU. By converting wave audio format (WAV) of Daic-woz database to the Melfrequency cepstrum (MFC). We have done the training and evaluated the 4 model architectures and compared the results between non-augmented and augmented datasets. The highest accuracy was obtained from 1D CNN with a non-data augmentation of 95%, and a 2D CNN with a data augmentation of 75%. These results confirm that human voices can differentiate between depression and non-depression.