Paing, May

Preferred name

Paing, May

Alternative Name

Paing, May Phu

Paing, May Phyu

Main Affiliation

School of Engineering

may.pa@kmitl.ac.th

Now showing 1 - 10 of 32

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Smart Cane for Assisting Visually Impaired People and the Blind
(2021-01-01)
Kramomthong, P.
;
Pintavirooj, C.
;
Paing, May Phu
The limited mobility of the visually impaired and blind makes daily activities such as avoiding obstacles, traveling, crossing the street, and dealing with an emergency difficult. Currently, there are active studies and developments about devices and technologies that increase deftness daily and travel independently. In this paper, we combine various functions into one device consisting of obstacle detection, navigation, and emergency calls to increase the safety and travel mobility of visually impaired people and the blind. The laboratory experiment results of whole functions were successful, and the device can alert the user with sound and vibration.
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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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Design and Development of an Assistive System Based on Eye Tracking
(2022-02-01)
Paing, May Phu
;
Juhong, Aniwat
;
Pintavirooj, Chuchart
This research concerns the design and development of an assistive system based on eye tracking, which can be used to improve the quality of life of disabled patients. With the use of their eye movement, whose function is not affected by their illness, patients are capable of communicating with and sending notifications to caretakers, controlling various appliances, including wheelchairs. The designed system is divided into two subsystems: Stationary and mobile assistive systems. Both systems provide a graphic user interface (GUI) that is used to link the eye tracker with the appliance control. There are six GUI pages for the stationary assistive system and seven for the mobile assistive system. GUI pages for the stationary assistive system include the home page, smart appliance page, eye-controlled television page, eye-controlled air conditional page, i-speak page and entertainment page. GUI pages for the mobile assistive system are similar to the GUI pages for the stationary assistive system, with the additional eye-controlled wheelchair page. To provide hand-free secure access, an authentication based on facial landmarks is developed. The operational test of the proposed assistive system provides successful and promising results.
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A Study of Pulmonary Nodules from Multi-slice Computed Tomography Using 3-D Structure
(2018-06-21)
Paing, May Phu
;
Choomchuay, Somsak
Multi-slice CT screening is an auspicious imaging test to detect the lung cancer. Formation of the pulmonary nodules in the lungs is the first suspicious sign of the lung cancer. The nodules appear as round or irregular shaped spots having size up to 30 mm on the CT scan. As the rate of cancer cases increases day by day, the manual detection of pulmonary nodules becomes a struggling way for the radiologists. The proposed method aims to develop an automated system to detect the pulmonary nodules using image processing techniques. Moreover, the needs of the conventional 2D detection are figured out and compensated by creating 3D structure.
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Detection of lesions and classification of diabetic retinopathy using fundus images
(2017-02-21)
Paing, May Phu
;
Choomchuay, Somsak
;
Rapeeporn Yodprom, M. D.
Diabetes retinopathy is a retinal disease that is affected by diabetes on the eyes. The main risk of the disease can lead to blindness. Detection the disease at early stage can rescue the patients from loss of vision. The major purpose of this paper is to automatically detect as well as to classify the severity of diabetic retinopathy. At first, the lesions on the retina especially blood vessels, exudates and microaneurysms are extracted. Features such as area, perimeter and count from these lesions are used to classify the stages of the disease by applying artificial neural network (ANN). We used 214 fundus images from DIARECTDB1 and local databases. We found that the system can give the classification accuracy of 96% and it supports a great help to ophthalmologists.
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Deep fusion feature extraction for caries detection on dental panoramic radiographs
(2021-03-01)
Bui, Toan Huy
;
Hamamoto, Kazuhiko
;
Paing, May Phu
Caries is the most well-known disease and relates to the oral health of billions of people around the world. Despite the importance and necessity of a well-designed detection method, studies in caries detection are still limited and show a restriction in performance. In this paper, we proposed a computer-aided diagnosis (CAD) method to detect caries among normal patients using dental radiographs. The proposed method mainly consists of two processes: feature extraction and classification. In the feature extraction phase, the chosen 2D tooth image was employed to extract deep activated features using a deep pre-trained model and geometric features using mathematic formulas. Both feature sets were then combined, called fusion feature, to complement each other defects. Then, the optimal fusion feature set was fed into well-known classification models such as support vector machine (SVM), k-nearest neighbor (KNN), decision tree (DT), Naïve Bayes (NB), and random forest (RF) to determine the best classification model that fit the fusion features set and perform the most preeminent result. The results show 91.70%, 90.43%, and 92.67% for accuracy, sensitivity, and specificity, respectively. The proposed method has outperformed the previous stateof- the-art and shows promising results when none of the measured factors is less than 90%; therefore, the method is promising for dentists and capable of wide-scale implementation caries detection in hospitals.
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Adenoma Dysplasia Grading of Colorectal Polyps Using Fast Fourier Convolutional ResNet (FFC-ResNet)
(2023-01-01)
Paing, May Phu
;
Pintavirooj, Chuchart
Colorectal polyps are precursor lesions of colorectal cancer; hence, early detection and dysplasia grading of polyps are essential for determining cancer risk, the possibility of developing subsequent polyps, and follow-up recommendations. The significant contribution of this study is the development of an enhanced deep-learning model called Fast Fourier Convolutional ResNet (FFC-ResNet) to classify dysplasia grades of polyps. It is based on the ResNet-50 architecture and uses cross-feature fusion, which combines local features extracted by traditional spatial convolution with global features extracted by Fourier convolution. Due to the compensatory effect between local and global features, the learnability and performance of FFC-ResNet have increased. The proposed FFC-ResNet was developed and tested using UniToPatho, a dataset containing 7000 μm and 800 μm hematoxylin-and-eosin (H&E)-stained colorectal images. And a favorable performance of sensitivity 0.95, specificity 0.93, balance accuracy 0.94, precision 0.95, F1 score 0.95, and AUC 0.99 was obtained using 800 μm polyp patches.
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Parking Time Violation Tracking Using YOLOv8 and Tracking Algorithms
(2023-07-01)
Sharma, Nabin
;
Baral, Sushish
;
Paing, May Phu
;
Chawuthai, Rathachai
The major problem in Thailand related to parking is time violation. Vehicles are not allowed to park for more than a specified amount of time. Implementation of closed-circuit television (CCTV) surveillance cameras along with human labor is the present remedy. However, this paper presents an approach that can introduce a low-cost time violation tracking system using CCTV, Deep Learning models, and object tracking algorithms. This approach is fairly new because of its appliance of the SOTA detection technique, object tracking approach, and time boundary implementations. YOLOv8, along with the DeepSORT/OC-SORT algorithm, is utilized for the detection and tracking that allows us to set a timer and track the time violation. Using the same apparatus along with Deep Learning models and algorithms has produced a better system with better performance. The performance of both tracking algorithms was well depicted in the results, obtaining MOTA scores of (1.0, 1.0, 0.96, 0.90) and (1, 0.76, 0.90, 0.83) in four different surveillance data for DeepSORT and OC-SORT, respectively.
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A computer aided diagnosis system for detection of lung nodules from series of CT slices
(2017-11-03)
Paing, May Phu
;
Choomchuay, Somsak
The proposed system aims to detect the lung nodules from a series of CT scan images. Otsu's thresholding and morphological operations are applied for nodules segmentation. After segmentation, the objects that do not hold the possibility to be nodules are removed. Geometric, histogram as well as texture features are then extracted for benign and malignant nodules classification. Multilayer Perceptron (MLP) is used for classification and the accuracy 95% has been achieved.
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Ground glass opacity (GGO) nodules detection from lung CT scans
(2017-07-01)
Paing, May Phu
;
Choomchuay, Somsak
Ground glass opacity (GGO) nodules have a higher possibility of malignancy compared to other types of nodules appeared in the lung cancer. They are very effortful to detect due to their hazy structures and unclear margins. 65% of lung cancers are missed by the radiologists with the faint appearance of the GGO. Consequently, the detection of GGO is a critical issue and a striving task for the radiologists. This research proposes an automatic detection of GGO remained after the detection of solid opacity. Simple thresholding based on grey levels and mathematical image subtraction are applied for segmentation. Possible false after segmentation are reduced by the support vector machine (SVM). In total 37 GGOs, only 2 are missed by proposed segmentation and the false reduction performance is 94%.

Paing, May

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