Phasukkit, Pattarapong

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Phasukkit, Pattarapong
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Phasukkit, P.
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pattarapong.ph@kmitl.ac.th

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    Item type:Publication,
    3D Kinect Camera Scheme with Time-Series Deep-Learning Algorithms for Classification and Prediction of Lung Tumor Motility
    (2022-04-01)
    Puangragsa, Utumporn
    ;
    Setakornnukul, Jiraporn
    ;
    Dankulchai, Pittaya
    ;
    Phasukkit, Pattarapong
    This paper proposes a time-series deep-learning 3D Kinect camera scheme to classify the respiratory phases with a lung tumor and predict the lung tumor displacement. Specifically, the proposed scheme is driven by two time-series deep-learning algorithmic models: The respiratory-phase classification model and the regression-based prediction model. To assess the performance of the proposed scheme, the classification and prediction models were tested with four categories of datasets: Patient-based datasets with regular and irregular breathing patterns; and pseudopatientbased datasets with regular and irregular breathing patterns. In this study, 'pseudopatients' refer to a dynamic thorax phantom with a lung tumor programmed with varying breathing patterns and breaths per minute. The total accuracy of the respiratory-phase classification model was 100%, 100%, 100%, and 92.44% for the four dataset categories, with a corresponding mean squared error (MSE), mean absolute error (MAE), and coefficient of determination (R<sup>2</sup>) of 1.2-1.6%, 0.65-0.8%, and 0.97-0.98, respectively. The results demonstrate that the time-series deep-learning classification and regression-based prediction models can classify the respiratory phases and predict the lung tumor displacement with high accuracy. Essentially, the novelty of this research lies in the use of a low-cost 3D Kinect camera with time-series deep-learning algorithms in the medical field to efficiently classify the respiratory phase and predict the lung tumor displacement.