Maneerat, Noppadol

Preferred name

Maneerat, Noppadol

Main Affiliation

School of Engineering

noppadol.ma@kmitl.ac.th

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Suitable Supervised Machine Learning Techniques for Malignant Mesothelioma Diagnosis
(2019-01-10)
Win, Khin Yadanar
;
Maneerat, Noppadol
;
Choomchuay, Somsak
;
Sreng, Syna
;
Hamamoto, Kazuhiko
Malignant Mesothelioma (MM) is a rare, aggressive cancer that grows in the lining of the internal organs such as lung, abdomen or heart. Fousing on MM diagnosis, in this paper, we investigate multiple machine learning methods and compare for accurate MM diagnosis results. Seven machine learning algorithms namely (i) Linear Discriminant Analysis (LDA), (ii) Naïve Bayes, (iii) K Nearest Neighborhood (KNN), (iv) Support Vector Machine (SVM), (v) Decision Tree (DT), (vi) Logistic Regression (LogR) and (vii) Random forest (RF) algorithms are exploited. The experiments dataset containing 324 cases with 34 features and six performance measures are used to assess the accuracy of evaluated classifiers. The average accuracy of LDA, NB, KNN, SVM, DT, LogR and RF are 61.73%, 67.90%, 91.36%, 100%, 100%, 100% and 100%, respectively. In addition, the computational complexity of each method is also analyzed. Each algoritm is judged based on its classification accuracy and computational complexity. It is found that SVM, DT, LogR and RF outperform the others and even previous studies.
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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
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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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Classification of Cotton Wool Spots Using Principal Components Analysis and Support Vector Machine
(2019-01-10)
Sreng, Syna
;
Maneerat, Noppadol
;
Win, Khin Yadanar
;
Hamamoto, Kazuhiko
;
Panjaphongse, Ronakorn
Diabetic retinopathy is a complication of the eye damage and can lead to being blindness if it is late for treatment. Microaneurysms, exudates, hemorrhages and cotton wool spots are the lesions associated with diabetic retinopathy. Numerous studies have been done on the detection of microaneurysms, and hemorrhages, as well as exudates whereas only a few research works for detection of cotton wool spots, mainly because of the fact that its appearances are difficult to filter out from the background and not clearly visible. In this paper, an algorithm is proposed to detect cotton wool spots based on integrating principal components analysis and support vector machine. First, preprocessing is performed to enhance the retinal images. Then adaptive thresholding method is used to roughly extract the cotton wool spot from the background. Support vector machine and principal components analysis are further applied respectively to select the important features from morphologies, first-order statistics, gray level occurrence matrix and lacunarity. The proposed method was evaluated with local and DIARETDB1 datasets containing 289 images. Given a success rate of accuracy 90.47 %, sensitivity 85.29%, and specificity 90.12% with the average computational time 16.47 seconds per image on cotton wool spots detection, this system performed better by comparing to the previous research works.
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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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Deep learning for optic disc segmentation and glaucoma diagnosis on retinal images
(2020-07-01)
Sreng, Syna
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Maneerat, Noppadol
;
Hamamoto, Kazuhiko
;
Win, Khin Yadanar
Glaucoma is a major global cause of blindness. As the symptoms of glaucoma appear, when the disease reaches an advanced stage, proper screening of glaucoma in the early stages is challenging. Therefore, regular glaucoma screening is essential and recommended. However, eye screening is currently subjective, time-consuming and labor-intensive and there are insufficient eye specialists available. We present an automatic two-stage glaucoma screening system to reduce the workload of ophthalmologists. The system first segmented the optic disc region using a DeepLabv3+ architecture but substituted the encoder module with multiple deep convolutional neural networks. For the classification stage, we used pretrained deep convolutional neural networks for three proposals (1) transfer learning and (2) learning the feature descriptors using support vector machine and (3) building ensemble of methods in (1) and (2). We evaluated our methods on five available datasets containing 2787 retinal images and found that the best option for optic disc segmentation is a combination of DeepLabv3+ and MobileNet. For glaucoma classification, an ensemble of methods performed better than the conventional methods for RIM-ONE, ORIGA, DRISHTI-GS1 and ACRIMA datasets with the accuracy of 97.37%, 90.00%, 86.84% and 99.53% and Area Under Curve (AUC) of 100%, 92.06%, 91.67% and 99.98%, respectively, and performed comparably with CUHKMED, the top team in REFUGE challenge, using REFUGE dataset with an accuracy of 95.59% and AUC of 95.10%.
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Brain tumor detection based on Naïve Bayes classification
(2019-07-01)
Zaw, Hein Tun
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Maneerat, Noppadol
;
Win, Khin Yadanar
Brain cancer is caused by the population of abnormal cells called glial cells that takes place in the brain. Over the years, the number of patients who have brain cancer is increasing with respect to the aging population, is a worldwide health problem. The objective of this paper is to develop a method to detect the brain tissues which are affected by cancer especially for grade-4 tumor, Glioblastoma multiforme (GBM). GBM is one of the most malignant cancerous brain tumors as they are fast growing and more likely to spread to other parts of the brain. In this paper, Naïve Bayes classification is utilized for recognition of a tumor region accurately that contains all spreading cancerous tissues. Brain MRI database, preprocessing, morphological operations, pixel subtraction, maximum entropy threshold, statistical features extraction, and Naïve Bayes classifier based prediction algorithm are used in this research. The goal of this method is to detect the tumor area from different brain MRI images and to predict that detected area whether it is a tumor or not. When compared to other methods, this method can properly detect the tumor located in different regions of the brain including the middle region (aligned with eye level) which is the significant advantage of this method. When tested on 50 MRI images, this method develops 81.25% detection rate on tumor images and 100% detection rate on non-tumor images with the overall accuracy 94%.

Maneerat, Noppadol

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