Paing, May

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Paing, May
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Paing, May Phu
Paing, May Phyu
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may.pa@kmitl.ac.th

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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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    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%.
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    Improved Random Forest (RF) classifier for imbalanced classification of lung nodules
    (2018-08-13)
    Paing, May Phu
    ;
    Choomchuay, Somsak
    Computer-aided detection (CAD) for lung cancer acts a dynamic research in biomedical engineering. These CADs generally occur imbalanced data classification because there are a large number of false lesions which are non-nodules (majority class), compared to the actual nodules (minority class). This paper proposes an improved random forest (RF) classifier to solve the learning bias problem of the imbalanced classification. The proposed RF applies sampling and three feature selection schemes namely Relief, Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) to improve the classification performance.
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    Classification of margin characteristics from 3D pulmonary nodules
    (2017-12-19)
    Paing, May Phu
    ;
    Choomchuay, Somsak
    Detection of pulmonary nodules has played a significant role in lung cancer diagnosis because nodules are the first suspicious symptoms for the likelihood of cancer. Margin characteristics of the pulmonary nodules provide essential radiological features to determine the possibility of malignancy. In general, benign nodules hold quite smooth margins whilst malignant ones hold irregular margins. The main objective of this research is to classify different margin types of pulmonary nodules by observing the 3D structure. Nodule candidates from 2D lung CT slices are segmented firstly and then stacked to form a 3D image. Geometric features of the 3D nodule are extracted and fed into the support vector machine (SVM) classifier to classify the margin types. The proposed method can provide the classification accuracy of 90.9%.
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    Comparison of Sampling Methods for Imbalanced Data Classification in Random Forest
    (2019-01-10)
    Paing, May Phu
    ;
    Pintavirooj, C.
    ;
    Tungjitkusolmun, Supan
    ;
    Choomchuay, Somsak
    ;
    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.