Maneerat, Noppadol

Preferred name

Maneerat, Noppadol

Main Affiliation

School of Engineering

noppadol.ma@kmitl.ac.th

Now showing 1 - 10 of 11

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A "dual-acceptor channel" membraneless gas-diffusion unit for simultaneous determination of ethanol and acetaldehyde in liquors using reverse flow injection
(2018-01-01)
Choengchan, Nathawut
;
Poontong, Bangerdsuk
;
Mathaweesansurn, Arjnarong
;
Maneerat, Noppadol
;
Motomizu, Shoji
A new design of membraneless gas-diffusion unit with dual acceptor channels for separation, collection and simultaneous determination of two volatile analytes in liquid sample is presented. The unit is comprised of three parallel channels in a closed module. A sample is aspirated into the central channel and two kinds of reagents are introduced into the other two channels. Two analytes are isolated from the sample matrix by diffusion into head-space and absorbed into the specific reagents. Non-absorbed vapor is released by opening the programmable controlled lid. The unit was applied to liquors for measurement of ethanol and acetaldehyde using reverse flow injection. Dichromate and nitroprusside were exploited as reagents for colorimetric detection of ethanol and acetaldehyde, respectively. Good linearity ranges (r2 > 0.99) with high precision (RSD < 2%) and high accuracy (recovery: 90 - 105%) were achieved. The results were compared to the results by GC-FID and no significant difference was observed by paired t-test (95% confidence).
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Automated diabetic retinopathy screening system using hybrid simulated annealing and ensemble bagging classifier
(2018-07-22)
Sreng, Syna
;
Maneerat, Noppadol
;
Hamamoto, Kazuhiko
;
Panjaphongse, Ronakorn
Diabetic Retinopathy (DR) is the leading cause of blindness in working-age adults globally. Primary screening of DR is essential, and it is recommended that diabetes patients undergo this procedure at least once per year to prevent vision loss. However, in addition to the insufficient number of ophthalmologists available, the eye examination itself is labor-intensive and time-consuming. Thus, an automated DR screening method using retinal images is proposed in this paper to reduce the workload of ophthalmologists in the primary screening process and so that ophthalmologists may make effective treatment plans promptly to help prevent patient blindness. First, all possible candidate lesions of DR were segmented from the whole retinal image using a combination of morphological-top-hat and Kirsch edge-detection methods supplemented by pre- and post-processing steps. Then, eight feature extractors were utilized to extract a total of 208 features based on the pixel density of the binary image as well as texture, color, and intensity information for the detected regions. Finally, hybrid simulated annealing was applied to select the optimal feature set to be used as the input to the ensemble bagging classifier. The evaluation results of this proposed method, on a dataset containing 1200 retinal images, indicate that it performs better than previous methods, with an accuracy of 97.08%, a sensitivity of 90.90%, a specificity of 98.92%, a precision of 96.15%, an F-measure of 93.45% and the area under receiver operating characteristic curve at 98.34%.
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Hybrid learning of hand-crafted and deep-activated features using particle swarm optimization and optimized support vector machine for tuberculosis screening
(2020-09-01)
Win, Khin Yadanar
;
Maneerat, Noppadol
;
Hamamoto, Kazuhiko
;
Sreng, Syna
Tuberculosis (TB) is a leading infectious killer, especially for people with Human Immunodeficiency Virus (HIV) and Acquired Immunodeficiency Syndrome (AIDS). Early diagnosis of TB is crucial for disease treatment and control. Radiology is a fundamental diagnostic tool used to screen or triage TB. Automated chest x-rays analysis can facilitate and expedite TB screening with fast and accurate reports of radiological findings and can rapidly screen large populations and alleviate a shortage of skilled experts in remote areas. We describe a hybrid feature-learning algorithm for automatic screening of TB in chest x-rays: it first segmented the lung regions using the DeepLabv3+ model. Then, six sets of hand-crafted features from statistical textures, local binary pattern, GIST, histogram of oriented gradients (HOG), pyramid histogram of oriented gradients and bags of visual words (BoVW), and nine sets of deep-activated features from AlexNet, GoogLeNet, InceptionV3, XceptionNet, ResNet-50, SqueezeNet, ShuffleNet, MobileNet, and DenseNet, were extracted. The dominant features of each feature set were selected using particle swarm optimization, and then separately input to an optimized support vector machine classifier to label 'normal' and 'TB' x-rays. GIST, HOG, BoVW from hand-crafted features, and MobileNet and DenseNet from deep-activated features performed better than the others. Finally, we combined these five best-performing feature sets to build a hybrid-learning algorithm. Using the Montgomery County (MC) and Shenzen datasets, we found that the hybrid features of GIST, HOG, BoVW, MobileNet and DenseNet, performed best, achieving an accuracy of 92.5% for the MC dataset and 95.5% for the Shenzen dataset.
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A mobile phone-based analyzer for quantitative determination of urinary albumin using self-calibration approach
(2017-04-01)
Mathaweesansurn, Arjnarong
;
Maneerat, Noppadol
;
Choengchan, Nathawut
This work demonstrates use of a smart mobile phone installed with an Android application, termed ‘Albumin smart test’, as an analyzer for quantitative determination of urinary albumin. The reaction between albumin and tetrabromophenolphthalein ethyl ester (TBPE) in the presence of Triton X-100 was employed for detection principle.The mobile phone was exploited with the sample cassette and the test paper. One sample cassette composes of two holders for accommodation of control and test samples. The test paper was designed in order to contain standard colorimetric strip and space for situating the sample cassette. Optical images of the strip and the samples were simultaneously captured in a single shot by a digital camera of the mobile phone and were digitally processed by the developed application for quantification of the albumin concentration based on self-calibration approach. With the advantage of self-calibration, the albumin test by our mobile phone can be performed in ambient light without using any extra module integrated with lighting control device. The other advantages are portability, ease of implementation and rapid analysis (3 min) with high precision (RSD ≤ 2.5%) and high accuracy (Recovery = 98.7% ± 1.6). The mobile device was successfully applied to diagnosis of microalbuminuria.
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Glioma Brain Tumor Classification Using Convolution Neural Network and Majority Voting
(2025-01-01)
Pilaoon, Pongsak
;
Hamamoto, Kazuhiko
;
Maneerat, Noppadol
Glioma brain tumors are malignant diseases for which early detection and instant treatment will increase the survival rate. Several studies have reported the efficiency of deep learning convolutional neural networks (CNN) in diagnosing brain tumors using magnetic resonance imaging (MRI). In this study, we investigated the potential of state-of-the-art classifiers to achieve the highest accuracy in the detection of brain tumors using MRI. For this purpose, we introduced a comparative study of eight state-of-the-art classifiers. The methodology comprised three different approaches: 1) an imbalanced dataset, 2) a balanced dataset using image augmentation, and 3) ensemble learning using the best of the top five models for majority hard and soft voting. The dataset comprised converted MRI data from the repository of molecular brain neoplasia data (REMBRANDT) and brain tumor segmentation 2021 (BraTS) databases. An increasing number of MRI images and datasets has prevented overfitting. Initially, a preprocessing stage morphological operation and contrast-limited adaptive histogram equalization (CLAHE) algorithms were used to remove skeletons and artifacts and optimize the image contrast for readiness classification. The stochastic gradient descent with the momentum algorithm option was used to train the network. The trained model was used to predict the testing dataset, and the results from each pretrained network were evaluated. The experimental results demonstrated that the prediction accuracy of the trained network was significantly improved using a balanced training dataset. The discriminative image region used to interpret the predicted result using the gradient-weighted class activation mapping (Grad-CAM) algorithm was proposed in the final stage for trustworthiness. The experimental results showed that the best approach was inceptionV3 with a balanced dataset. The accuracy, sensitivity, specificity, and area under the curve were 99.73%, 99.61%, 100%, and 1.00, respectively.
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Cotton wool spots detection in diabetic retinopathy based on adaptive thresholding and ant colony optimization coupling support vector machine
(2019-06-01)
Sreng, Syna
;
Maneerat, Noppadol
;
Hamamoto, Kazuhiko
;
Panjaphongse, Ronakorn
Diabetic retinopathy is the major issue of diabetes-induced blindness worldwide but is curable if detected in time. Cotton wool spots (CWSs) are the critical lesions of diabetic retinopathy, which indicate not only advanced nonproliferative but also preproliferative diabetic retinopathy. It is crucial to detect CWSs for grading the severity of diabetic retinopathy. By grading the severity of diabetic retinopathy accurately, the eye specialist can make an effective treatment plan to protect the patient's vision against blindness. CWSs detection remains challenging because of their uneven appearance, in which some CWSs are not clearly visible and some resemble hard exudates. This paper proposed an automatic CWS detection method based on adaptive thresholding and ant colony optimization (ACO) coupled with support vector machine (SVM). One-hundred and sixty-two features from five feature sets, namely morphologies, first-order statistics, gray-level co-occurrence matrix, gray-level run length matrix, and lacunarity, are extracted, and then four feature selection methods,namely genetic algorithm, particle swarm optimization, stepwise method, and ACO, are coupled with SVM classifiers. The evaluation results of the proposed methods on local, standard diabetic retinopathy database calibration level 1, and high-resolution fundus image database datasets containing 319 images indicate that ACO coupling cubic SVM performs better than the other pairs with sensitivity 90.16%, specificity 97.92%, accuracy 96.96%, and area under receiver operating characteristic curve 97.19%. © 2019 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.
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Composition of service and protocol specifications in asynchronous communication system
(2004-01-01)
Maneerat, Noppadol
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Varakulsiripunth, Ruttikorn
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Bista, Bhed Bahadur
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Takahashi, Kaoru
;
Kato, Yasushi
One of the important techniques in communication system design is the composition of service and protocol specifications. In this paper, we have presented a new approach to the composition technique based on the weak bisimulation concept The main objective is to combine service specifications and protocol specifications individually and simultaneously. The composition technique can maintain the equivalence between the composed service and protocol specifications. LOTOS language terms are utilized to describe the communication specifications. The application on the asynchronous model is presented. Moreover, a support system of the composition technique is developed and presented in this paper.
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Ensemble deep learning for the detection of COVID-19 in unbalanced chest X-ray dataset
(2021-11-01)
Win, Khin Yadanar
;
Maneerat, Noppadol
;
Sreng, Syna
;
Hamamoto, Kazuhiko
The ongoing COVID-19 pandemic has caused devastating effects on humanity worldwide. With practical advantages and wide accessibility, chest X-rays (CXRs) play vital roles in the diagnosis of COVID-19 and the evaluation of the extent of lung damages incurred by the virus. This study aimed to leverage deep-learning-based methods toward the automated classification of COVID-19 from normal and viral pneumonia on CXRs, and the identification of indicative regions of COVID-19 biomarkers. Initially, we preprocessed and segmented the lung regions usingDeepLabV3+ method, and subsequently cropped the lung regions. The cropped lung regions were used as inputs to several deep convolutional neural networks (CNNs) for the prediction of COVID-19. The dataset was highly unbalanced; the vast majority were normal images, with a small number of COVID-19 and pneumonia images. To remedy the unbalanced distribution and to avoid biased classification results, we applied five different approaches: (i) balancing the class using weighted loss; (ii) image augmentation to add more images to minority cases; (iii) the undersampling of majority classes; (iv) the oversampling of minority classes; and (v) a hybrid resampling approach of oversampling and undersampling. The best-performing methods from each approach were combined as the ensemble classifier using two voting strategies. Finally, we used the saliency map of CNNs to identify the indicative regions of COVID-19 biomarkers which are deemed useful for interpretability. The algorithms were evaluated using the largest publicly available COVID-19 dataset. An ensemble of the top five CNNs with image augmentation achieved the highest accuracy of 99.23% and area under curve (AUC) of 99.97%, surpassing the results of previous studies.
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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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Blood warmer using peltiers
(2014-01-01)
Suksantisakul, Park
;
Maneerat, Noppadol
;
Nakprik, Thongchan
;
Varakulsiripunth, Ruttikorn
The purpose of this paper is introduces blood warmer system to easy for anesthesiologists and patients for infusion and facilitate of anesthesiologists work for using blood bag. The system is contained in a rectangular box, inside of which consists of two separate chambers: one housing the electronics inside have; circulating fans, heat sinks, transformer (12v, 100w, 8.3amp) and a PCB with an SG3525 IC for regulation of temperatures in the adjacent chamber and the other the blood warming components inside have is fitted with a shaker, EPDM(Ethylene Propylene Diene Monomer) cushion sheets, a digital thermometer, and two thermo-sensor. Two peltiers and EPDM cushion sheets are employed in the proposed system to quicken the warming of blood bags. Nonetheless, three experiments were conducted with three different blood warming configurations: using one peltier without the EPDM cushion sheets, two peltiers without the EPDM cushion sheets, and two peltiers with the EPDM cushion sheets. The finding indicates that the use of two peltiers together with the plates produces the best results. © 2014 SERSC.

Maneerat, Noppadol

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