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 - 6 of 6

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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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Automated Caries Screening Using Ensemble Deep Learning on Panoramic Radiographs
(2022-10-01)
Bui, Toan Huy
;
Hamamoto, Kazuhiko
;
Paing, May Phu
Caries prevention is essential for oral hygiene. A fully automated procedure that reduces human labor and human error is needed. This paper presents a fully automated method that segments tooth regions of interest from a panoramic radiograph to diagnose caries. A patient’s panoramic oral radiograph, which can be taken at any dental facility, is first segmented into several segments of individual teeth. Then, informative features are extracted from the teeth using a pre-trained deep learning network such as VGG, Resnet, or Xception. Each extracted feature is learned by a classification model such as random forest, k-nearest neighbor, or support vector machine. The prediction of each classifier model is considered as an individual opinion that contributes to the final diagnosis, which is decided by a majority voting method. The proposed method achieved an accuracy of 93.58%, a sensitivity of 93.91%, and a specificity of 93.33%, making it promising for widespread implementation. The proposed method, which outperforms existing methods in terms of reliability, and can facilitate dental diagnosis and reduce the need for tedious procedures.
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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
;
Tungjitkusolmun, Supan
;
Bui, Toan Huy
;
Visitsattapongse, Sarinporn
;
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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Multi-Disease Classification of COVID-19 in Chest Radiographs using Ensemble of Optimized Deep Learning Models
(2023-01-01)
Bui, Toan Huy
;
Hamamoto, Kazuhiko
;
Bui, Linh Khanh
;
Paing, May Phu
In recent years, COVID-19 has become the top concern of almost everyone around the world. Identifying COVID-19 infections is extremely important for appropriate treatment methods. Furthermore, classifying Covid along with other lung diseases also plays an important role. This article proposes a method for diagnosing COVID-19 and other common lung diseases on chest X-ray images using CADs. CNN models are used to learn informative features from patient chest X-ray images. The performance of diagnosis is optimized using the area under the curve maximization and proximal epoch stochastic optimization. Later, optimized results are employed to compute the most probabilities prediction and produce the latest result. The proposed method was tested on a large public radiograph dataset and reached 98.07%, 96.92%, and 96.36% for accuracy, precision, and recall, respectively. The performance is promising and comparable to other previous research but in a more complex dataset. Overall, this proposed method is trustworthy for medical doctors on COVID-19 detection among lung disease problems.
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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
;
Sento, Adna
;
Bui, Toan Huy
;
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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Tooth Localization using YOLOv3 for Dental Diagnosis on Panoramic Radiographs
(2022-01-01)
Bui, Toan Huy
;
Hamamoto, Kazuhiko
;
Paing, May Phu
Oral health is one of most major concerns that affect the life quality of billions of people around the world. Diagnosis treatment usually takes time due to the lack of doctors compared to a huge number of patients. Many researchers proposed methods to make an early disease detection for patients to assist doctors using computer aid diagnosis (CAD). However, most previous methods are not end-to-end methods and still require human involvement. The biggest challenge is that most researchers do not provide a good tooth detection technique before diagnosis. Therefore, the main objective, that builds a system to assist doctors, remains unaccomplished or just fairly successful. This paper proposed a detection method to localize the tooth using the Yolov3 model as a base network in the dental panoramic radiograph. The method consists of two main parts: image preprocessing and tooth localization. Firstly, because deep learning requires a big dataset, the original image is applied augmentation technique to improve the size of the dataset as well as diversity. Then, each image is resized to fit the input layer of the network; however, to prevent the information loss and boost the performance, we keep the original ratio of the images and change the ratio of the input layer in the model that can fit the image ratio. Next, we feed images into Yolov3, which is specially modified to fit the problem, for training. We add more detection heads into the backbone and concatenate the previous head detection’s result with a proper layer to produce a more preeminent result. The final assessment shows an impressive result that the method reaches 95.58% and 94.90% for precision and recall, respectively. As a result, our proposed method is more reliable and practical in the tooth localization field, as well as helpful to reduce the doctor's effort.

Paing, May

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