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Author: search.filters.author.Chomkwah, WanwaleeSubject: search.filters.subject.Bacteria

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    Item type:Publication,
    A Comparison of Deep Learning CNN Architecture Models for Classifying Bacteria
    (2022-01-01)
    Poomrittigul, Suvit
    ;
    Chomkwah, Wanwalee
    ;
    Tanpatanan, Tananan
    ;
    Sakorntanant, Sakda
    ;
    Treebupachatsakul, Treesukon
    Since identifying bacteria from a patient's sample for medical diagnosis purposes by the traditional approach is time-consuming and requires the pathologist's expertise to do the bacteria identification procedure. Thus, involving the deep learning model reported the capability of multi-class image classification allows us to reduce the time and increase the prediction accuracy of the bacteria identification process. This research includes 35 different bacteria species and 6 different Convolutional Neural Network (CNN) architectures. Convolutional Neural Network (CNN) architectures are LeNet-5, AlexNet, VGG-16, VGG-19, ResNet-18, and ResNet-34. The results confirmed the perceptional performance by applying Stratified K-fold cross validation with VGG-16 and observing the multi-class performance with the AUC-ROC score.