Development of Debiasing Technique for Lung Nodule Chest X-ray Datasets to Generalize Deep Learning Models.
- Publisher:
- MDPI
- Publication Type:
- Journal Article
- Citation:
- Sensors (Basel), 2023, 23, (14), pp. 6585
- Issue Date:
- 2023-07-21
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Full metadata record
Field | Value | Language |
---|---|---|
dc.contributor.author | Horry, MJ | |
dc.contributor.author |
Chakraborty, S |
|
dc.contributor.author |
Pradhan, B |
|
dc.contributor.author | Paul, M | |
dc.contributor.author | Zhu, J | |
dc.contributor.author | Loh, HW | |
dc.contributor.author | Barua, PD | |
dc.contributor.author | Acharya, UR | |
dc.date.accessioned | 2024-02-06T23:03:50Z | |
dc.date.available | 2023-07-20 | |
dc.date.available | 2024-02-06T23:03:50Z | |
dc.date.issued | 2023-07-21 | |
dc.identifier.citation | Sensors (Basel), 2023, 23, (14), pp. 6585 | |
dc.identifier.issn | 1424-8220 | |
dc.identifier.issn | 1424-8220 | |
dc.identifier.uri | http://hdl.handle.net/10453/175393 | |
dc.description.abstract | Screening programs for early lung cancer diagnosis are uncommon, primarily due to the challenge of reaching at-risk patients located in rural areas far from medical facilities. To overcome this obstacle, a comprehensive approach is needed that combines mobility, low cost, speed, accuracy, and privacy. One potential solution lies in combining the chest X-ray imaging mode with federated deep learning, ensuring that no single data source can bias the model adversely. This study presents a pre-processing pipeline designed to debias chest X-ray images, thereby enhancing internal classification and external generalization. The pipeline employs a pruning mechanism to train a deep learning model for nodule detection, utilizing the most informative images from a publicly available lung nodule X-ray dataset. Histogram equalization is used to remove systematic differences in image brightness and contrast. Model training is then performed using combinations of lung field segmentation, close cropping, and rib/bone suppression. The resulting deep learning models, generated through this pre-processing pipeline, demonstrate successful generalization on an independent lung nodule dataset. By eliminating confounding variables in chest X-ray images and suppressing signal noise from the bone structures, the proposed deep learning lung nodule detection algorithm achieves an external generalization accuracy of 89%. This approach paves the way for the development of a low-cost and accessible deep learning-based clinical system for lung cancer screening. | |
dc.format | Electronic | |
dc.language | eng | |
dc.publisher | MDPI | |
dc.relation.ispartof | Sensors (Basel) | |
dc.relation.isbasedon | 10.3390/s23146585 | |
dc.rights | info:eu-repo/semantics/openAccess | |
dc.subject | 0301 Analytical Chemistry, 0502 Environmental Science and Management, 0602 Ecology, 0805 Distributed Computing, 0906 Electrical and Electronic Engineering | |
dc.subject.classification | Analytical Chemistry | |
dc.subject.classification | 3103 Ecology | |
dc.subject.classification | 4008 Electrical engineering | |
dc.subject.classification | 4009 Electronics, sensors and digital hardware | |
dc.subject.classification | 4104 Environmental management | |
dc.subject.classification | 4606 Distributed computing and systems software | |
dc.subject.mesh | Humans | |
dc.subject.mesh | Deep Learning | |
dc.subject.mesh | Neural Networks, Computer | |
dc.subject.mesh | X-Rays | |
dc.subject.mesh | Early Detection of Cancer | |
dc.subject.mesh | Lung Neoplasms | |
dc.subject.mesh | Lung | |
dc.subject.mesh | Lung | |
dc.subject.mesh | Humans | |
dc.subject.mesh | Lung Neoplasms | |
dc.subject.mesh | X-Rays | |
dc.subject.mesh | Early Detection of Cancer | |
dc.subject.mesh | Deep Learning | |
dc.subject.mesh | Neural Networks, Computer | |
dc.subject.mesh | Humans | |
dc.subject.mesh | Deep Learning | |
dc.subject.mesh | Neural Networks, Computer | |
dc.subject.mesh | X-Rays | |
dc.subject.mesh | Early Detection of Cancer | |
dc.subject.mesh | Lung Neoplasms | |
dc.subject.mesh | Lung | |
dc.title | Development of Debiasing Technique for Lung Nodule Chest X-ray Datasets to Generalize Deep Learning Models. | |
dc.type | Journal Article | |
utslib.citation.volume | 23 | |
utslib.location.activity | Switzerland | |
utslib.for | 0301 Analytical Chemistry | |
utslib.for | 0502 Environmental Science and Management | |
utslib.for | 0602 Ecology | |
utslib.for | 0805 Distributed Computing | |
utslib.for | 0906 Electrical and Electronic Engineering | |
pubs.organisational-group | University of Technology Sydney | |
pubs.organisational-group | University of Technology Sydney/Faculty of Engineering and Information Technology | |
pubs.organisational-group | University of Technology Sydney/Faculty of Engineering and Information Technology/School of Civil and Environmental Engineering | |
pubs.organisational-group | University of Technology Sydney/Strength - CAMGIS - Centre for Advanced Modelling and Geospatial lnformation Systems | |
pubs.organisational-group | University of Technology Sydney/Faculty of Engineering and Information Technology/School of Information, Systems and Modelling | |
utslib.copyright.status | open_access | * |
dc.date.updated | 2024-02-06T23:03:49Z | |
pubs.issue | 14 | |
pubs.publication-status | Published online | |
pubs.volume | 23 | |
utslib.citation.issue | 14 |
Abstract:
Screening programs for early lung cancer diagnosis are uncommon, primarily due to the challenge of reaching at-risk patients located in rural areas far from medical facilities. To overcome this obstacle, a comprehensive approach is needed that combines mobility, low cost, speed, accuracy, and privacy. One potential solution lies in combining the chest X-ray imaging mode with federated deep learning, ensuring that no single data source can bias the model adversely. This study presents a pre-processing pipeline designed to debias chest X-ray images, thereby enhancing internal classification and external generalization. The pipeline employs a pruning mechanism to train a deep learning model for nodule detection, utilizing the most informative images from a publicly available lung nodule X-ray dataset. Histogram equalization is used to remove systematic differences in image brightness and contrast. Model training is then performed using combinations of lung field segmentation, close cropping, and rib/bone suppression. The resulting deep learning models, generated through this pre-processing pipeline, demonstrate successful generalization on an independent lung nodule dataset. By eliminating confounding variables in chest X-ray images and suppressing signal noise from the bone structures, the proposed deep learning lung nodule detection algorithm achieves an external generalization accuracy of 89%. This approach paves the way for the development of a low-cost and accessible deep learning-based clinical system for lung cancer screening.
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