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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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Comparisons of pap smear classification with deep learning models
(2019-04-01)
Promworn, Yuttachon
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Pattanasak, Satjana
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Pintavirooj, Chuchart
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Piyawattanametha, Wibool
We presented a comparative work of deep learning models for Pap smear classification. The benchmark parameters used to compare are accuracy, specificity, computation time, and sensitivity. Five convolution neural network models were employed to compare performance in detecting the presence of cervical precancerous or cancerous cells from a Pap smear database. The best deep learning model for multiclass classification is the densenet161 with an efficiency of 68.0% which will use to implement in our custom-made whole slide imager.
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Footprint Identification using Deep Learning
(2019-01-10)
Keatsamarn, Tanapon
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Pintavirooj, Chuchart
Human footprint is the biometric system of the individual person. Everyone has specific footprints. It can be used instead of password-based authentication in the security system such as a user authentication for the financial transaction. The password-based system cannot verify that the person who entered the password is valid or not. Therefore the biometric system is more secure than the password-based system. For that reason, it's interesting to use footprint image in the creating of the footprint-based identification system. In this paper, the convolutional neural network training is used for deep learning classification. Convolutional neural networks are essential for deep learning and suited for image recognition.

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