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Deep Learning Method for Early Detection of Lung Cancer using Deep Learning Algorithms

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Bibliographic Details
Published in:International Journal of Engineering and Manufacturing
Format: Online Article RSS Article
Published: 2026
Subjects:
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container_title International Journal of Engineering and Manufacturing
description
discipline_display Technology & Engineering
discipline_facet Technology & Engineering
format Online Article
RSS Article
genre Journal Article
id rss_article:12666
institution FRELIP
journal_source_facet International Journal of Engineering and Manufacturing
publishDate 2026
publishDateSort 2026
record_format rss_article
spellingShingle Deep Learning Method for Early Detection of Lung Cancer using Deep Learning Algorithms
Manufacturing and Technology
Technology & Engineering — Computing
Technology & Engineering
sub_discipline_display Technology & Engineering — Computing
sub_discipline_facet Technology & Engineering — Computing
subject_display Manufacturing and Technology
Technology & Engineering — Computing
Technology & Engineering
Manufacturing and Technology
Technology & Engineering — Computing
Technology & Engineering
subject_facet Manufacturing and Technology
Technology & Engineering — Computing
Technology & Engineering
title Deep Learning Method for Early Detection of Lung Cancer using Deep Learning Algorithms
title_auth Deep Learning Method for Early Detection of Lung Cancer using Deep Learning Algorithms
title_full Deep Learning Method for Early Detection of Lung Cancer using Deep Learning Algorithms
title_fullStr Deep Learning Method for Early Detection of Lung Cancer using Deep Learning Algorithms
title_full_unstemmed Deep Learning Method for Early Detection of Lung Cancer using Deep Learning Algorithms
title_short Deep Learning Method for Early Detection of Lung Cancer using Deep Learning Algorithms
title_sort deep learning method for early detection of lung cancer using deep learning algorithms
topic Manufacturing and Technology
Technology & Engineering — Computing
Technology & Engineering
url https://www.mecs-press.org/ijem/ijem-v16-n2/v16n2-11.html