Narkthewan, Athasart
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Preferred name
Narkthewan, Athasart
Main Affiliation
Email
athasart.na@kmitl.ac.th
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Item type:Publication, Apply PLC to Control 3-Axis Machines(2025-01-01); ; This report presents a method of using PLC (Programable Logic Control) to control the work instead of the 3-Axis machine position controller. PLC is popularly used in various types of control systems. It has a lot of control capabilities, is efficient, stable, durable, easy to use, and cheap. It is the use of commands to control the work of all 3 axes, 2 axes, and 1 axle. The commands used for moving in both straight lines and curves, and there are other devices that need to be controlled together with the machine that is being controlled. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Detection of hard exudate for diabetic retinopathy using unsupervised classification method(2020-07-01); ; Kimpan, ChomDiabetic retinopathy (DR) causes retinal disorders such as blood vessel blockage, the leaks of blood, and the proteins in water bleeding into the tissues of retina. All of the symptoms lead to the destruction of retina resulting in reduced visibility or finally lose vision. Therefore, this study presents an image processing method to extract hard exudates in the retinal image, which is a serious symptom of diabetic retinopathy using an unsupervised classification method. The proposed hard exudates extraction method composes of 3 steps. Firstly, the optic disc similar to hard exudate is eliminated from the retinal image. Subsequently, the green channel of the RGB color model is selected for data analysis because it represents all hard exudates better than the red and blue channels. The features of hard exudates in the retinal image are then extracted by various methods such as dilation, erosion, entropy analysis, and standard deviation analysis and it also appeared in many dimensions. Finally, the proposed method uses k-mean, which is an unsupervised classification technique for hard exudates clustering. The determination of hard exudates from the retinal image is achieved using two datasets (DIARETDB0 and DIARETDB1). These datasets are usually used for algorithm efficiency analysis to retinal image evaluation. The results show that the maximum specificity is approximate 97%. It indicates that the proposed method can be applied for the automatic detection of diabetic retinopathy. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, A Cascade of Encoder–Decoder with Atrous Convolution and Ensemble Deep Convolutional Neural Networks for Tuberculosis Detection(2025-07-01); ; Hamamoto, KazuhikoTuberculosis (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. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Glioma Brain Tumor Classification using Transfer Learning(2024-01-01);