Pasupa, Kitsuchart

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Pasupa, Kitsuchart
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kitsuchart.pa@kmitl.ac.th

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    Item type:Publication,
    An Image Segment-based Classification for Chest X-Ray Image
    (2020-11-19)
    Kittiworapanya, Phongsathorn
    ;
    Pasupa, Kitsuchart
    In late 2019, the first case of COVID-19 was confirmed in Wuhan, China. The number of cases has been rapidly growing since then. Molecular and antigen testing methods are very accurate for the diagnosis of COVID-19. However, with sudden increases of infected cases, laboratory-based molecular test and COVID-19 test kits are in short supply. Because the virus affects an infected patient's lung, interpreting images obtained from Computed Tomography Scanners and Chest X-ray Radiography (CXR) machines can be an alternative for diagnosis. However CXR interpretation requires experts and the number of experts is limited. Therefore, automatic detection of COVID-19 from CXR images is required. We describe a system for automatic detection of COVID-19 from CXR images. It first segmented images to select only the lung. The segmented part was then fed into a multiclass classification module, which worked well with samples obtained from various sources, which had different aspect ratios, contrast and viewpoints. The system also handled the unbalanced dataset-only a small fraction of images showed COVID-19. Our system achieved 92% of F1-score and 88.1% Marco F1-score on the 3rd Deep Learning and AI Summer/Winter School Hackathon Phase 3-Multi-class COVID-19 Chest X-ray challenge public leaderboard.
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    Item type:Publication,
    Particle Size Estimation in Mixed Commercial Waste Images Using Deep Learning
    (2021-06-29)
    Kittiworapanya, Phongsathorn
    ;
    Pasupa, Kitsuchart
    ;
    Auer, Peter
    We assessed several state-of-the-art deep learning algorithms and computer vision techniques for estimating the particle size of mixed commercial waste from images. In waste management, the first step is often coarse shredding, using the particle size to set up the shredder machine. The difficulty is separating the waste particles in an image, which can not be performed well. This work focused on estimating size by using the texture from the input image, captured at a fixed height from the camera lens to the ground. We found that EfficientNet achieved the best performance of 0.72 on F1-Score and 75.89% on accuracy.