Classifying chronic obstructive pulmonary disease status using computed tomography imaging and convolutional neural networks: comparison of model input image types and training data severity

The CNN-based model with the combined 2D tMPR images and 3D lung view as input image types had the highest performance for COPD classification, highlighting the importance of airway information and that the fusion of different types of information as input image can improve CNN-based model performance. In addition, models trained on CanCOLD demonstrated strong generalization to the more severe SPIROMICS cohort, whereas training on SPIROMICS resulted in lower performance when tested on CanCOLD. These findings suggest that training on milder COPD cases may improve classification performance across the disease spectrum.

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Publication Details

Journal
Journal of Medical Imaging
Published
2025-05-22
DOI
https://doi.org/10.1117/1.jmi.12.3.034502
Citations
1
Primary Topic
Radiomics and Machine Learning in Medical Imaging
Type
article
Field-Weighted Citation Impact
1.36

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article

Classifying chronic obstructive pulmonary disease status using computed tomography imaging and convolutional neural networks: comparison of model input image types and training data severity

Jean Bourbeau, Sara Rezvanjou, Joseph M. Reinhardt, Miranda Kirby et al.
1 citations
Journal of Medical Imaging
Radiomics and Machine Learning in Medical Imaging
1.36
article

Classifying chronic obstructive pulmonary disease status using computed tomography imaging and convolutional neural networks: comparison of model input image types and training data severity

Jean Bourbeau, Sara Rezvanjou, Joseph M. Reinhardt, Miranda Kirby, S. Peterson, Wan C. Tan, James C. Hogg, Amir Moslemi
article en
1 citations

Abstract

The CNN-based model with the combined 2D tMPR images and 3D lung view as input image types had the highest performance for COPD classification, highlighting the importance of airway information and that the fusion of different types of information as input image can improve CNN-based model performance. In addition, models trained on CanCOLD demonstrated strong generalization to the more severe SPIROMICS cohort, whereas training on SPIROMICS resulted in lower performance when tested on CanCOLD. These findings suggest that training on milder COPD cases may improve classification performance across the disease spectrum.

Journal of Medical ImagingVol. 12(03)
University of Iowa (US), University of British Columbia (CA), Royal Victoria Hospital (CA), Vida Diagnostics (United States) (US), Sunnybrook Hospital (CA), Sunnybrook Research Institute, Toronto Metropolitan University (CA)
National Heart, Lung, and Blood Institute
Good health and well-being
Openalex Percentile: Top 20%
Radiomics and Machine Learning in Medical Imaging
1.36
DOISourceOpenAlex
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Classifying chronic obstructive pulmonary disease status using computed tomography imaging and convolutional neural networks: comparison of model input image types and training data severity — Jean Bourbeau, Sara Rezvanjou, et al. · Journal of Medical Imaging (2025) | TGRS Research Map | TGRS