Please use this identifier to cite or link to this item: https://hdl.handle.net/1959.11/59306
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dc.contributor.authorHorry, Michael Jen
dc.contributor.authorChakraborty, Subrataen
dc.contributor.authorPradhan, Biswajeeten
dc.contributor.authorPaul, Manoranjanen
dc.contributor.authorZhu, Jingen
dc.contributor.authorLoh, Hui Wenen
dc.contributor.authorBarua, Prabal Dattaen
dc.contributor.authorAcharya, U Rajendraen
dc.date.accessioned2024-05-15T08:32:14Z-
dc.date.available2024-05-15T08:32:14Z-
dc.date.issued2023-07-21-
dc.identifier.citationSensors, 23(14), p. 1-21en
dc.identifier.issn1424-8220en
dc.identifier.issn1424-8239en
dc.identifier.urihttps://hdl.handle.net/1959.11/59306-
dc.description.abstract<p>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.</p>en
dc.languageenen
dc.publisherMDPI AGen
dc.relation.ispartofSensorsen
dc.rightsAttribution 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.titleDevelopment of Debiasing Technique for Lung Nodule Chest X-ray Datasets to Generalize Deep Learning Modelsen
dc.typeJournal Articleen
dc.identifier.doi10.3390/s23146585en
dcterms.accessRightsUNE Greenen
local.contributor.firstnameMichael Jen
local.contributor.firstnameSubrataen
local.contributor.firstnameBiswajeeten
local.contributor.firstnameManoranjanen
local.contributor.firstnameJingen
local.contributor.firstnameHui Wenen
local.contributor.firstnamePrabal Dattaen
local.contributor.firstnameU Rajendraen
local.profile.schoolSchool of Science and Technologyen
local.profile.emailschakra3@une.edu.auen
local.output.categoryC1en
local.record.placeauen
local.record.institutionUniversity of New Englanden
local.publisher.placeSwitzerlanden
local.identifier.runningnumber6585en
local.format.startpage1en
local.format.endpage21en
local.peerreviewedYesen
local.identifier.volume23en
local.identifier.issue14en
local.access.fulltextYesen
local.contributor.lastnameHorryen
local.contributor.lastnameChakrabortyen
local.contributor.lastnamePradhanen
local.contributor.lastnamePaulen
local.contributor.lastnameZhuen
local.contributor.lastnameLohen
local.contributor.lastnameBaruaen
local.contributor.lastnameAcharyaen
dc.identifier.staffune-id:schakra3en
local.profile.orcid0000-0002-0102-5424en
local.profile.roleauthoren
local.profile.roleauthoren
local.profile.roleauthoren
local.profile.roleauthoren
local.profile.roleauthoren
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local.profile.roleauthoren
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local.identifier.unepublicationidune:1959.11/59306en
dc.identifier.academiclevelAcademicen
dc.identifier.academiclevelAcademicen
dc.identifier.academiclevelAcademicen
dc.identifier.academiclevelAcademicen
dc.identifier.academiclevelAcademicen
dc.identifier.academiclevelAcademicen
dc.identifier.academiclevelAcademicen
dc.identifier.academiclevelAcademicen
local.title.maintitleDevelopment of Debiasing Technique for Lung Nodule Chest X-ray Datasets to Generalize Deep Learning Modelsen
local.output.categorydescriptionC1 Refereed Article in a Scholarly Journalen
local.search.authorHorry, Michael Jen
local.search.authorChakraborty, Subrataen
local.search.authorPradhan, Biswajeeten
local.search.authorPaul, Manoranjanen
local.search.authorZhu, Jingen
local.search.authorLoh, Hui Wenen
local.search.authorBarua, Prabal Dattaen
local.search.authorAcharya, U Rajendraen
local.open.fileurlhttps://rune.une.edu.au/web/retrieve/712228a1-c6f0-4d1e-9866-cf2760052664en
local.uneassociationYesen
local.atsiresearchNoen
local.sensitive.culturalNoen
local.year.published2023en
local.fileurl.openhttps://rune.une.edu.au/web/retrieve/712228a1-c6f0-4d1e-9866-cf2760052664en
local.fileurl.openpublishedhttps://rune.une.edu.au/web/retrieve/712228a1-c6f0-4d1e-9866-cf2760052664en
local.subject.for20204601 Applied computingen
local.profile.affiliationtypeExternal Affiliationen
local.profile.affiliationtypeUNE Affiliationen
local.profile.affiliationtypeExternal Affiliationen
local.profile.affiliationtypeExternal Affiliationen
local.profile.affiliationtypeExternal Affiliationen
local.profile.affiliationtypeExternal Affiliationen
local.profile.affiliationtypeExternal Affiliationen
local.profile.affiliationtypeExternal Affiliationen
local.date.moved2024-06-18en
Appears in Collections:Journal Article
School of Science and Technology
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