Paing, May

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Paing, May
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Paing, May Phu
Paing, May Phyu
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may.pa@kmitl.ac.th

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    Item type:Publication,
    Histopathological Classification of Colorectal Polyps using Deep Learning
    (2023-01-01)
    Paing, May Phu
    ;
    Cho, One Sun
    ;
    Cho, Jae Wan
    Early diagnosis and classification of colorectal polyps are critical in reducing the morbidity and mortality rate of colorectal cancer (CRC). This paper proposes an automated method for histopathologically classifying colorectal polyps from 7000 μm H&E-stained images. First, a number of state-of-the-art deep learning models are developed and fine-tuned using transfer learning and ImageNet pre-trained weights. Subsequently, a baseline architecture is selected by comparing the trained models, and its performance is then optimized using data augmentation methods such as rotation, rescaling, mixup and cutout. Moreover, an extended variant of the adaptive moment estimation (Adam) optimizer called rectified Adam (Radam) and label smoothing are also used to boost the model performance. Based on the experimentation results using an open dataset, the proposed method achieved an accuracy of 90%, a precision of 90%, a recall of 89% and an F1-score of 0.91%.
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    Classification of White Blood Cells: A Comprehensive Study Using Transfer Learning Based on Convolutional Neural Networks
    (2022-12-01)
    Tamang, Thinam
    ;
    Baral, Sushish
    ;
    Paing, May Phu
    White blood cells (WBCs) in the human immune system defend against infection and protect the body from external hazardous objects. They are comprised of neutrophils, eosinophils, basophils, monocytes, and lymphocytes, whereby each accounts for a distinct percentage and performs specific functions. Traditionally, the clinical laboratory procedure for quantifying the specific types of white blood cells is an integral part of a complete blood count (CBC) test, which aids in monitoring the health of people. With the advancements in deep learning, blood film images can be classified in less time and with high accuracy using various algorithms. This paper exploits a number of state-of-the-art deep learning models and their variations based on CNN architecture. A comparative study on model performance based on accuracy, F1-score, recall, precision, number of parameters, and time was conducted, and DenseNet161 was found to demonstrate a superior performance among its counterparts. In addition, advanced optimization techniques such as normalization, mixed-up augmentation, and label smoothing were also employed on DenseNet to further refine its performance.