Treebupachatsakul, Treesukon

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Treebupachatsakul, Treesukon
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Treebupachatsakul, T.
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treesukon.tr@kmitl.ac.th

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    Item type:Publication,
    Real-Time White Blood Cell Classification with YOLO
    (2025-01-01)
    Eamkong, Anoma
    ;
    Pintavirooj, Chuchart
    ;
    Treebupachatsakul, Treesukon
    White blood cell (WBC) classification plays a crucial role in diagnosing various hematological conditions, including infections, immune disorders, and leukemia. This study presents an automated approach for WBC detection and classification using the YOLOv5 deep learning model. The system integrates a 1.3MP microscope camera with a stepper motor-driven platform for real-time imaging and classification. The dataset consists of five WBC types: basophils, eosinophils, lymphocytes, monocytes, and neutrophils, with image enhancement and data augmentation applied to improve model performance. The trained YOLOv5 model achieved a classification accuracy of 92.61% and a validation accuracy of 95.86%, demonstrating high precision and recall in WBC identification. The results indicate that this system can effectively automate WBC analysis, reducing manual effort and improving diagnostic accuracy. This approach has potential applications in clinical hematology, offering a rapid and reliable method for WBC classification.
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    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.