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https://hdl.handle.net/1959.11/59306
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DC Field | Value | Language |
---|---|---|
dc.contributor.author | Horry, Michael J | en |
dc.contributor.author | Chakraborty, Subrata | en |
dc.contributor.author | Pradhan, Biswajeet | en |
dc.contributor.author | Paul, Manoranjan | en |
dc.contributor.author | Zhu, Jing | en |
dc.contributor.author | Loh, Hui Wen | en |
dc.contributor.author | Barua, Prabal Datta | en |
dc.contributor.author | Acharya, U Rajendra | en |
dc.date.accessioned | 2024-05-15T08:32:14Z | - |
dc.date.available | 2024-05-15T08:32:14Z | - |
dc.date.issued | 2023-07-21 | - |
dc.identifier.citation | Sensors, 23(14), p. 1-21 | en |
dc.identifier.issn | 1424-8220 | en |
dc.identifier.issn | 1424-8239 | en |
dc.identifier.uri | https://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.language | en | en |
dc.publisher | MDPI AG | en |
dc.relation.ispartof | Sensors | en |
dc.rights | Attribution 4.0 International | * |
dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | * |
dc.title | Development of Debiasing Technique for Lung Nodule Chest X-ray Datasets to Generalize Deep Learning Models | en |
dc.type | Journal Article | en |
dc.identifier.doi | 10.3390/s23146585 | en |
dcterms.accessRights | UNE Green | en |
local.contributor.firstname | Michael J | en |
local.contributor.firstname | Subrata | en |
local.contributor.firstname | Biswajeet | en |
local.contributor.firstname | Manoranjan | en |
local.contributor.firstname | Jing | en |
local.contributor.firstname | Hui Wen | en |
local.contributor.firstname | Prabal Datta | en |
local.contributor.firstname | U Rajendra | en |
local.profile.school | School of Science and Technology | en |
local.profile.email | schakra3@une.edu.au | en |
local.output.category | C1 | en |
local.record.place | au | en |
local.record.institution | University of New England | en |
local.publisher.place | Switzerland | en |
local.identifier.runningnumber | 6585 | en |
local.format.startpage | 1 | en |
local.format.endpage | 21 | en |
local.peerreviewed | Yes | en |
local.identifier.volume | 23 | en |
local.identifier.issue | 14 | en |
local.access.fulltext | Yes | en |
local.contributor.lastname | Horry | en |
local.contributor.lastname | Chakraborty | en |
local.contributor.lastname | Pradhan | en |
local.contributor.lastname | Paul | en |
local.contributor.lastname | Zhu | en |
local.contributor.lastname | Loh | en |
local.contributor.lastname | Barua | en |
local.contributor.lastname | Acharya | en |
dc.identifier.staff | une-id:schakra3 | en |
local.profile.orcid | 0000-0002-0102-5424 | en |
local.profile.role | author | en |
local.profile.role | author | en |
local.profile.role | author | en |
local.profile.role | author | en |
local.profile.role | author | en |
local.profile.role | author | en |
local.profile.role | author | en |
local.profile.role | author | en |
local.identifier.unepublicationid | une:1959.11/59306 | en |
dc.identifier.academiclevel | Academic | en |
dc.identifier.academiclevel | Academic | en |
dc.identifier.academiclevel | Academic | en |
dc.identifier.academiclevel | Academic | en |
dc.identifier.academiclevel | Academic | en |
dc.identifier.academiclevel | Academic | en |
dc.identifier.academiclevel | Academic | en |
dc.identifier.academiclevel | Academic | en |
local.title.maintitle | Development of Debiasing Technique for Lung Nodule Chest X-ray Datasets to Generalize Deep Learning Models | en |
local.output.categorydescription | C1 Refereed Article in a Scholarly Journal | en |
local.search.author | Horry, Michael J | en |
local.search.author | Chakraborty, Subrata | en |
local.search.author | Pradhan, Biswajeet | en |
local.search.author | Paul, Manoranjan | en |
local.search.author | Zhu, Jing | en |
local.search.author | Loh, Hui Wen | en |
local.search.author | Barua, Prabal Datta | en |
local.search.author | Acharya, U Rajendra | en |
local.open.fileurl | https://rune.une.edu.au/web/retrieve/712228a1-c6f0-4d1e-9866-cf2760052664 | en |
local.uneassociation | Yes | en |
local.atsiresearch | No | en |
local.sensitive.cultural | No | en |
local.year.published | 2023 | en |
local.fileurl.open | https://rune.une.edu.au/web/retrieve/712228a1-c6f0-4d1e-9866-cf2760052664 | en |
local.fileurl.openpublished | https://rune.une.edu.au/web/retrieve/712228a1-c6f0-4d1e-9866-cf2760052664 | en |
local.subject.for2020 | 4601 Applied computing | en |
local.profile.affiliationtype | External Affiliation | en |
local.profile.affiliationtype | UNE Affiliation | en |
local.profile.affiliationtype | External Affiliation | en |
local.profile.affiliationtype | External Affiliation | en |
local.profile.affiliationtype | External Affiliation | en |
local.profile.affiliationtype | External Affiliation | en |
local.profile.affiliationtype | External Affiliation | en |
local.profile.affiliationtype | External Affiliation | en |
local.date.moved | 2024-06-18 | en |
Appears in Collections: | Journal Article School of Science and Technology |
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File | Description | Size | Format | |
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openpublished/DevelopmentChakraborty2023JournalArticle.pdf | Published Version | 7.33 MB | Adobe PDF Download Adobe | View/Open |
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