Convolutional Neural Network-Based Lung Cancer Nodule Detection Based on Computer Tomography

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dc.contributor.authorAhmed, A.H.
dc.contributor.authorAlwan, H.B.
dc.contributor.authorÇakmak, M.
dc.date.accessioned2024-09-29T16:21:14Z
dc.date.available2024-09-29T16:21:14Z
dc.date.issued2023
dc.departmentKarabük Üniversitesien_US
dc.descriptionInternational Conference on Data Analytics and Management, ICDAM 2022 -- 25 June 2022 through 26 June 2022 -- Jelenia Góra -- 292549en_US
dc.description.abstractBecause of the great responsiveness of aspiratory knob location, computerized tomography (CT) is generally used to analyze cellular breakdown in the lungs without performing biopsy, which could make actual harm nerves and vessels. Notwithstanding, recognizing threatening and harmless aspiratory knobs stays troublesome. Since CT checks are regularly of low goal, it is challenging for radiologists to peruse the output picture’s subtleties. The proceeded with quick development of CT examine examination frameworks lately has made a squeezing need for cutting edge computational apparatuses to remove helpful highlights to help the radiologist in understanding advancement. PC-supported discovery (CAD) frameworks have been created to diminish notable mistakes by distinguishing the dubious highlights a radiologist searches for in a case survey. Our project aims to compare performance of various low memories, lightweight deep neural net (DNN) architectures for biomedical image analysis. It will involve networks like vanilla 2D CNN, U-Net, 2D SqueezeNet, and 2D MobileNet for two case classifications to discover the existence of lung cancer in patient CT scans of lungs with and without primary phase lung cancer. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.en_US
dc.identifier.doi10.1007/978-981-19-7615-5_8
dc.identifier.endpage102en_US
dc.identifier.isbn978-981197614-8
dc.identifier.issn2367-3370
dc.identifier.scopus2-s2.0-85152557579en_US
dc.identifier.scopusqualityQ4en_US
dc.identifier.startpage89en_US
dc.identifier.urihttps://doi.org/10.1007/978-981-19-7615-5_8
dc.identifier.urihttps://hdl.handle.net/20.500.14619/9635
dc.identifier.volume572en_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherSpringer Science and Business Media Deutschland GmbHen_US
dc.relation.ispartofLecture Notes in Networks and Systemsen_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectAI modelen_US
dc.subjectCAD systemen_US
dc.subjectComputer tomographyen_US
dc.subjectPulmonary noduleen_US
dc.titleConvolutional Neural Network-Based Lung Cancer Nodule Detection Based on Computer Tomographyen_US
dc.typeConference Objecten_US

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