ArticleSensors (Basel, Switzerland)2022
3D Kinect Camera Scheme with Time-Series Deep-Learning Algorithms for Classification and Prediction of Lung Tumor Motility.
Article in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
3 citing papers in PubMed.
- Robust human locomotion and localization activity recognition over multisensory.Frontiers in physiology · 2024Article
- Enhanced Deep-Learning-Based Automatic Left-Femur Segmentation Scheme with Attribute Augmentation.Sensors (Basel, Switzerland) · 2023Article
- Autoformer-Based Model for Predicting and Assessing Wheat Quality Changes of Pesticide Residues during Storage.Foods (Basel, Switzerland) · 2023Article
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Authors and funding
4 authors.
Funding
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 pseudopatient-based 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
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Registered trials
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