Narkthewan, Athasart

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Narkthewan, Athasart
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athasart.na@kmitl.ac.th

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    Item type:Publication,
    A Cascade of Encoder–Decoder with Atrous Convolution and Ensemble Deep Convolutional Neural Networks for Tuberculosis Detection
    (2025-07-01)
    Maneerat, Noppadol
    ;
    Narkthewan, Athasart
    ;
    Hamamoto, Kazuhiko
    Tuberculosis (TB) is the most serious worldwide infectious disease and the leading cause of death among people with HIV. Early diagnosis and prompt treatment can cut off the rising number of TB deaths, and analysis of chest X-rays is a cost-effective method. We describe a deep learning-based cascade algorithm for detecting TB in chest X-rays. Firstly, the lung regions were segregated from other anatomical structures by an encoder–decoder with an atrous separable convolution network—DeepLabv3+ with an XceptionNet backbone, DLabv3+X, and then cropped by a bounding box. Using the cropped lung images, we trained several pre-trained Deep Convolutional Neural Networks (DCNNs) on the images with hyperparameters optimized by a Bayesian algorithm. Different combinations of trained DCNNs were compared, and the combination with the maximum accuracy was retained as the winning combination. The ensemble classifier was designed to predict the presence of TB by fusing DCNNs from the winning combination via weighted averaging. Our lung segmentation was evaluated on three publicly available datasets: it provided better Intercept over Union (IoU) values: 95.1% for Montgomery County (MC), 92.8% for Shenzhen (SZ), and 96.1% for JSRT datasets. For TB prediction, our ensemble classifier produced a better accuracy of 92.7% for the MC dataset and obtained a comparable accuracy of 95.5% for the SZ dataset. Finally, occlusion sensitivity and gradient-weighted class activation maps (Grad-CAM) were generated to indicate the most influential regions for the prediction of TB and to localize TB manifestations.
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    Glioma Brain Tumor Classification using Transfer Learning
    (2024-01-01)
    Pilaoon, Pongsak
    ;
    Narkthewan, Athasart
    ;
    Wadlom, Noppanat
    ;
    Varakulsiripunth, Ruttikorn
    ;
    Hamamoto, Kazuhiko
    In this research the glioma brain tumor binary classification using transfer learning was introduced. The MRI image from 2 datasets comprised with REMBRANDT and BraTS2021 with increasing number of MRI images was proposed to prevent overfitting problem. MRI images were converted to JPEG format and heavily imbalanced with normal brain image is minority class. Morphological operation was used to remove skeletons and artifacts from brain region. We have introduced Contrast Limited Adaptive Histogram Equalization to preprocess and enhance contrast before classify using various CNNs. To handle imbalanced dataset problem, we proposed image augmentation to increase the number of images and obtain balanced dataset. The various CNNs transfer learning was implemented to classify glioma brain tumor. Finally, the best classifier is InceptionV3 with balanced dataset that obtained accuracy 99.19%, sensitivity 98.83%, and specificity 100% respectively, better than our past research work.