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Author: search.filters.author.Paing, May PhuSubject: search.filters.subject.Deep learning

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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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    Multi-Disease Classification of COVID-19 in Chest Radiographs using Ensemble of Optimized Deep Learning Models
    (2023-01-01)
    Bui, Toan Huy
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    Hamamoto, Kazuhiko
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    Bui, Linh Khanh
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    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
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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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    Deep fusion feature extraction for caries detection on dental panoramic radiographs
    (2021-03-01)
    Bui, Toan Huy
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    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.