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Author: search.filters.author.Poomrittigul, SuvitSubject: search.filters.subject.Deep learning

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    Deep learning-based object detection of restorative dental instruments with potential implications for workflow automation and infection control in dental supply units
    (2026-12-01)
    Poomrittigul, Suvit
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    Mittong, Sirawit
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    Thanathornwong, Bhornsawan
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    Suebnukarn, Siriwan
    This study presents a proof-of-concept deep learning approach for automated detection and classification of restorative dental instruments on standardized trays, aiming to support workflow automation and infection control in dental supply units. A dataset comprising 1,000 images and 14,000 annotated instances of restorative dental instruments across 14 categories was developed. The YOLOv8 model was trained and evaluated on this dataset using standard object detection metrics, including precision, recall, and mean average precision at IoU thresholds 0.5 (mAP@0.5) and 0.5:0.95 (mAP@[0.5:0.95]). To assess model advancement, YOLOv8 performance was compared against its predecessors, YOLOv5, YOLOv6, and YOLOv7, under identical experimental settings. A session-level data split was implemented as the primary evaluation to minimize data leakage and provide a realistic estimate of generalization across unseen tray configurations. The YOLOv8 model achieved highest mean average precision mAP@0.5 of 95.9% and mAP@[0.5:0.95] of 80.9%, demonstrating robust detection capability under both standard and stringent evaluation thresholds. Across instrument categories, YOLOv8 demonstrated precision ranging from 90.3% to 100% and recall from 80.6 to 98.5%. The findings demonstrate the feasibility of using YOLOv8 for automated restorative dental instrument detection as an early-stage tool for improving supply unit efficiency. While results indicate high detection accuracy and robustness, further validation in diverse clinical environments is needed. Future deployment should incorporate human-in-the-loop verification, audit trails, and error escalation mechanisms to ensure safe and accountable AI-assisted workflows.
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    Performance of deep learning models for the classification and object detection of different oral white lesions using photographic images
    (2025-12-01)
    Khovidhunkit, Siribang on Piboonniyom
    ;
    Phosri, Kunchidsong
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    Thanathornwong, Bhornsawan
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    Rungraungrayabkul, Dulyapong
    ;
    Poomrittigul, Suvit
    Computer vision adjunctive technology for oral lesion diagnoses has been developed to detect and identify Oral Potentially Malignant Disorders (OPMDs) and non-OPMDs. The early detection of OPMDs can reduce the risk of oral cancer development, improving the survival rate of the patients. This study aims to evaluate the computer vision technique in the white oral lesion domain within the scope of photographic images. Deep learning techniques for the classification of Convolution Neural Networks (CNNs) and transformer neural networks, and one-stage models of YOLOv7 and YOLOv8 were utilized to classify and detect five classes of OPMDs and non-OPMDs oral white lesions including oral leukoplakia, oral lichen planus, pseudomembranous candidiasis, oral ulcers covered with pseudomembrane and other white benign oral lesions. From the evaluation results of classification, the IFormerBase model achieves overperformance compared to CNN models with accuracy, precision, and F1 score of more than 80% on the test set. The best model for object detection is YOLOv7 with 84.5% mean Average Precision (mAP) at Intersection over Union (IoU) threshold of 0.3 and 74.5% at IoU of 0.5 on the test set. Object detection results reveal promising automatic oral lesion identification, which can be further developed to enhance the lesion screening system.
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    Microorganism image recognition based on deep learning application
    (2020-01-01)
    Treebupachatsakul, Treesukon
    ;
    Poomrittigul, Suvit
    The application of Machine Learning for microorganism, especially bacteria and yeast, recognition becomes attractive because it can reduce the analyzing time of microorganism classification and eliminates human error compare to the classic biological techniques. Therefore, the recognition of microorganism based on Deep Learning increases the efficiency and accuracy of diagnostic process of infected patient. This research studies the possibility to use image classification and deep learning method to recognize bacteria and yeast with the comparison of cell image data-quality between our standard-resolution dataset and high-resolution dataset. We purpose this implementation method of microorganism recognition system using Python programming and the Keras API with Tensorflow Machine Learning framework. The experimental results have shown that bacteria and yeast cell images from microscope are able to be recognized. From the experimental results compare the deep learning methodology of different quality image dataset, our standard resolution dataset could be applied for obtaining more than 80% accuracy of prediction bacteria and yeast.
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    Bacteria Classification using Image Processing and Deep learning
    (2019-06-01)
    Treebupachatsakul, Treesukon
    ;
    Poomrittigul, Suvit
    An automizing process for bacteria recognition becomes attractive to reduce the analyzing time and increase the accuracy of diagnostic process. This research study possibility to use image classification and deep learning method for classify genera of bacteria. We propose the implementation method of bacteria recognition system using Python programing and the Keras API with TensorFlow Machine Learning framework. The implementation results have confirmed that bacteria images from microscope are able to recognize the genus of bacterium. The experimental results compare the deep learning methodology for accuracy in bacteria recognition standard resolution image use case. Proposed method can be applied the high-resolution datasets till standard resolution datasets for prediction bacteria type. However, this first study is limited to only two genera of bacteria.