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 12

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Smart Cane for Assisting Visually Impaired People and the Blind
(2021-01-01)
Kramomthong, P.
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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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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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Adenoma Dysplasia Grading of Colorectal Polyps Using Fast Fourier Convolutional ResNet (FFC-ResNet)
(2023-01-01)
Paing, May Phu
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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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Automated segmentation of infarct lesions in t1‐weighted mri scans using variational mode decomposition and deep learning
(2021-03-02)
Paing, May Phu
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Tungjitkusolmun, Supan
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Bui, Toan Huy
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Visitsattapongse, Sarinporn
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Pintavirooj, Chuchart
Automated segmentation methods are critical for early detection, prompt actions, and immediate treatments in reducing disability and death risks of brain infarction. This paper aims to develop a fully automated method to segment the infarct lesions from T1‐weighted brain scans. As a key novelty, the proposed method combines variational mode decomposition and deep learning-based segmentation to take advantages of both methods and provide better results. There are three main technical contributions in this paper. First, variational mode decomposition is applied as a pre-processing to discriminate the infarct lesions from unwanted non‐infarct tissues. Second, overlapped patches strategy is proposed to reduce the workload of the deep‐learning‐based segmentation task. Finally, a three‐dimensional U‐Net model is developed to perform patch‐wise segmentation of infarct lesions. A total of 239 brain scans from a public dataset are utilized to develop and evaluate the proposed method. Empirical results reveal that the proposed automated segmentation can provide promising performances with an average dice similarity coefficient (DSC) of 0.6684, intersection over union (IoU) of 0.5022, and average symmetric surface distance (ASSD) of 0.3932, respectively.
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Automatic detection of pulmonary nodules using three-dimensional chain coding and optimized random forest
(2020-04-01)
Paing, May Phu
;
Hamamoto, Kazuhiko
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Tungjitkusolmun, Supan
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Visitsattapongse, Sarinporn
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Pintavirooj, Chuchart
The detection of pulmonary nodules on computed tomography scans provides a clue for the early diagnosis of lung cancer. Manual detection mandates a heavy radiological workload as it identifies nodules slice-by-slice. This paper presents a fully automated nodule detection with three significant contributions. First, an automated seeded region growing is designed to segment the lung regions from the tomography scans. Second, a three-dimensional chain code algorithm is implemented to refine the border of the segmented lungs. Lastly, nodules inside the lungs are detected using an optimized random forest classifier. The experiments for our proposed detection are conducted using 888 scans from a public dataset, and achieves a favorable result of 93.11% accuracy, 94.86% sensitivity, and 91.37% specificity, with only 0.0863 false positives per exam.
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Automatic detection of mediastinal lymph nodes using 3D convolutional neural network
(2019-09-16)
Paing, May Phu
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Pintavirooj, Chuchart
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Tungjitkusolmun, Supan
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Win, Kyi Pyar
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Hamamoto, Kazuhiko
Mediastinal lymph nodes are one of the most critical factors to identify the clinical stages of lung cancer. As the lymph nodes are low in attenuation and cluttering with various shapes and sizes, manual detection is usually error-prone and effort-intensive. This paper introduces a method for automatic detection of mediastinal lymph nodes by proposing three significant contributions. First, we constraint the detection area, mediastinal region, using greylevel thresholding. Next, we apply the watershed method and hessian eigenvalues to separate a cluster of lymph nodes. Finally, we build a three-dimensional convolutional neural network (3D CNN) to distinguish the actual lymph nodes from other false lesions. Our experiment is conducted using 70 CT exams containing 314 lymph nodes and achieved a favorable result with 94 % detection rate.
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Enhanced deep learning model for prediction of diabetic mellitus on optical coherence tomography angiography images
(2026-01-01)
Visitsattapongse, Sarinporn
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Rithcharung, Preeyarat
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Santiprabhob, Jeerunda
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Lertbannaphong, Ornsuda
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Sermsripong, Wasawat
Background: Diabetes mellitus (DM) is a chronic metabolic disease characterized by dysregulated blood glucose. Prolonged DM can lead to diabetic retinopathy (DR), in which retinal capillaries are damaged by sustained hyperglycemia. Optical coherence tomography angiography (OCTA) is a non-invasive imaging modality for visualizing retinal microvasculature and can detect early changes in both DM patients with and without DR. However, it requires expert evaluation, making early detection costly and time-consuming. This study aimed to develop a high-performance deep learning framework that can classify OCTA images into three groups of DM, such as normal, good glycemic control, and poor glycemic control. Methods: OCTA datasets of horizontal B-scans and en face scans from 300 participants aged 8–18 years were analyzed, including normal controls, DM patients with good glycemic control, and DM patients with poor control (HbA1c ≥8%). For each participant, a 3 mm × 3 mm foveal-centered en face image of the deep capillary plexus (DCP) and a horizontal B-scan through the foveal center of the right eye were selected. Several convolutional and transformer-based models were evaluated, with ConvNeXt (a ConvNet for the 2020s) chosen as the baseline for its superior performance. To enhance generalization and convergence, progressive resizing and the Lookahead optimization strategy were applied, while class-wise augmentation was used to balance the training set without altering the test distribution. Results: The baseline ConvNeXt achieved F1 scores of 0.7877 (B-scans) and 0.7424 (en face). After doing enhancement using progressive resizing and Lookahead optimization, performance improved to 0.8319 and 0.8567 (Wilcoxon signed-rank tests, P<0.05). Conclusions: Our proposed method for DM classification from OCTA images provided promising results while ensuring resource efficiency and rapid evaluation. Clinically, accurate classification of DM status is valuable for assessing the risk of DR progression. Thus, it can be served as an assistive tool for clinical decision support in DR management.
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Instance Segmentation of Multiple Myeloma Cells Using Deep-Wise Data Augmentation and Mask R-CNN
(2022-01-01)
Paing, May Phu
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Sento, Adna
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Bui, Toan Huy
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Pintavirooj, Chuchart
Multiple myeloma is a condition of cancer in the bone marrow that can lead to dysfunction of the body and fatal expression in the patient. Manual microscopic analysis of abnormal plasma cells, also known as multiple myeloma cells, is one of the most commonly used diagnostic methods for multiple myeloma. However, as it is a manual process, it consumes too much effort and time. Besides, it has a higher chance of human errors. This paper presents a computer-aided detection and segmentation of myeloma cells from microscopic images of the bone marrow aspiration. Two major contributions are presented in this paper. First, different Mask R-CNN models using different images, including original microscopic images, contrast-enhanced images and stained cell images, are developed to perform instance segmentation of multiple myeloma cells. As a second contribution, a deep-wise augmentation, a deep learning-based data augmentation method, is applied to increase the performance of Mask R-CNN models. Based on the experimental findings, the Mask R-CNN model using contrast-enhanced images combined with the proposed deep-wise data augmentation provides a superior performance compared to other models. It achieves a mean precision of 0.9973, mean recall of 0.8631, and mean intersection over union (IOU) of 0.9062.
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Comparison of Sampling Methods for Imbalanced Data Classification in Random Forest
(2019-01-10)
Paing, May Phu
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Pintavirooj, C.
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Tungjitkusolmun, Supan
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Choomchuay, Somsak
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Hamamoto, Kazuhiko
Imbalanced data classification is a serious and challenging task for most of the medical image diagnosis applications. They usually produce a larger number of false samples compared to the actual ones. That is the number of samples for the class of interest (minority) is significantly fewer than other types of class (majority). The classification performed using such data is called imbalanced data classification. As a consequence, the learning model bias towards the majority class and fails the classification of the minority class. Data sampling and ensemble methods are common ways to compensate for this issue. Random forest (RF), an ensemble of multiple decision trees, is very famous in both of the classification and regression problems because of its robust and accurate predictions. However, it also suffers class bias in the imbalanced data classification problems. This paper proposes and compares different sampling methods to solve the imbalanced data classification in RF.
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Automatic Cardiopulmonary Resuscitation System
(2022-01-01)
Keakultanes, Rawin
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Paing, May Phu
;
Pintavirooj, Chuchart
Cardiac arrest occurs when the heart unexpectedly malfunctions and stops breathing and pumping blood. Cardiopulmonary resuscitation, an emergency resuscitation procedure, must be performed as soon as possible when the patients suffer cardiac arrest to prevent brain damage or stop breathing. If not, it may lead a person dead even in a short time (from 8-10 minutes) of cardiac arrest. According to the American Heart Association, proper CPR must be performed at a depth of 5 cm in the sternum and a rate of chest compressions of 100 to 120 breaths per minute. Therefore, medical personnel or the person who performs CPR must be well-Trained to conduct proper CPR. For this reason, CPR machines are demanded to assist and support medical personnel in saving human lives. This paper will focus on developing an automated CPR machine and automatic resuscitation ventilator to ensure that patients receive proper CPR. The device will be wirelessly controlled by a mobile phone via Bluetooth and was able to press at a depth of 4-5 cm, which is a correct depth, and at a rate of 70 beats per minute. An Arduino Uno microcontroller and a linear motor are used to control the push of the device. The proposed automated CPR machine will be a cost-effective, fully functional CPR device that is easy to use and can help people or healthcare professionals perform proper CPR.

Paing, May

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