3D Kinect Camera Scheme with Time-Series Deep-Learning Algorithms for Classification and Prediction of Lung Tumor Motility
| dc.contributor.author | Puangragsa, Utumporn | |
| dc.contributor.author | Setakornnukul, Jiraporn | |
| dc.contributor.author | Dankulchai, Pittaya | |
| dc.contributor.author | Phasukkit, Pattarapong | |
| dc.date.accessioned | 2026-08-06T10:36:44Z | |
| dc.date.available | 2026-08-06T10:36:44Z | |
| dc.date.issued | 2022-04-01 | |
| dc.description.abstract | 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. | |
| dc.identifier.citation | Sensors, 22(8), 2022 | |
| dc.identifier.doi | 10.3390/s22082918 | |
| dc.identifier.issn | 14248220 | |
| dc.identifier.other | 2-s2.0-85128769463 | |
| dc.identifier.uri | https://dspace.kmitl.ac.th/handle/123456789/13150 | |
| dc.source | Sensors | |
| dc.subject | 3D camera | |
| dc.subject | Deep learning | |
| dc.subject | External surrogate | |
| dc.subject | Kinect V.2 | |
| dc.subject | Lung cancer | |
| dc.subject | Times-series deep learning | |
| dc.title | 3D Kinect Camera Scheme with Time-Series Deep-Learning Algorithms for Classification and Prediction of Lung Tumor Motility | |
| dc.type | Article |