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Author: search.filters.author.Thongserm, Petchanon

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    Feasibility of Prediction Model for Internal Tumor Target Volume from 4-D Computed Tomography of Lung cancer
    (2021-01-01)
    Puangragsa, Utumporn
    ;
    Lomvisai, Pitchayakorn
    ;
    Phasukkit, Pattarapong
    ;
    Puangragsa, Sarut
    ;
    Setakornnukul, Jiraporn
    4-Dimensional computed tomography (4DCT) is the most common technique to determine organ movement due to breathing motion. However, the ability of 4DCT to acquire CT images as a function of the respiratory phase increases higher radiation dose. To reduce the patient's radiation dose, this study created lung motion prediction models used to estimate tumor target movement in ten respiratory phases by detecting only external organ movement during a complete respiration cycle without radiation with Kinect. The average overall amplitude difference between RPM and Kinect signals in the phantom experiment was 0.02 ± 0.1 mm. F1 score of 100% for all most all classifications except classification 2,3,6,7 and 8 of 85%,83%,90%, 84%,85% where irregular breathing pattern. Essentially, the proposed tumor movement scheme's total accuracy (average of F1 scores) is 92.7 %. Deep learning model can predict tumor motion range and classification zone by used detection of the external respiratory signal
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    Hand Movement Classification Base on EEG Signals using Deep Learning and Dimensional Reduction Technique
    (2019-11-01)
    Boonme, Phattraporn
    ;
    Thongserm, Petchanon
    ;
    Arunsuriyasak, Peerachai
    ;
    Phasukkit, Pattarapong
    This research is presented the bio-signal activities of arm movements by using deep learning for classification between right-arm and left-arm. It's well-known that Electroencephalography (EEG) shows neural oscillation behaviors in electrical voltage form. Also, Brain-Computer Interface (BCI) is direct communication between neural oscillation and computer to control machines without physical movements. So, this paper aims to present the classification method of EEG signals data to develop a BCI in the future. By using deep learning to classification data is classified into raise the right arm, raise the left arm. And decrease EEG signal data by using Principal Component Analysis (PCA). PCA can reduce the data size of EEG signal from 1000x28 to 28x28. Experimental result of classification has accuracy 90.86% and 94.71%