Enhancing lung cancer diagnosis with data fusion and mobile edge computing using DenseNet and CNN
Enhancing lung cancer diagnosis with data fusion and mobile edge computing using DenseNet and CNN
About this item
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Author / Creator
Publisher
Berlin/Heidelberg: Springer Berlin Heidelberg
Journal title
Language
English
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Publication information
Publisher
Berlin/Heidelberg: Springer Berlin Heidelberg
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Scope and Contents
Contents
The recent advancements in automated lung cancer diagnosis through the application of Convolutional Neural Networks (CNN) on Computed Tomography (CT) scans have marked a significant leap in medical imaging and diagnostics. The precision of these CNN-based classifiers in detecting and analyzing lung cancer symptoms has opened new avenues in early de...
Alternative Titles
Full title
Enhancing lung cancer diagnosis with data fusion and mobile edge computing using DenseNet and CNN
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TN_cdi_doaj_primary_oai_doaj_org_article_e0882d7b3d404ed99bfa16086e08b678
Permalink
https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_doaj_primary_oai_doaj_org_article_e0882d7b3d404ed99bfa16086e08b678
Other Identifiers
ISSN
2192-113X
E-ISSN
2192-113X
DOI
10.1186/s13677-024-00597-w
