Evaluating CNN Architectures and Hyperparameter Tuning for Enhanced Lung Cancer Detection Using Tran...
Evaluating CNN Architectures and Hyperparameter Tuning for Enhanced Lung Cancer Detection Using Transfer Learning
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Publisher
New York: Hindawi Limited
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Language
English
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Publisher
New York: Hindawi Limited
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Accurate lung cancer detection is vital for timely diagnosis and treatment. This study evaluates the performance of six convolutional neural network (CNN) architectures, ResNet‐50, VGG‐16, ResNet‐101, VGG‐19, DenseNet‐201, and EfficientNet‐B4, using the LIDC‐IDRI dataset. Models were assessed both in their base forms and with transfer learning. The...
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Evaluating CNN Architectures and Hyperparameter Tuning for Enhanced Lung Cancer Detection Using Transfer Learning
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TN_cdi_doaj_primary_oai_doaj_org_article_df8cd686b154422b8f7ab51f5e428a41
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https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_doaj_primary_oai_doaj_org_article_df8cd686b154422b8f7ab51f5e428a41
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ISSN
2090-0147
E-ISSN
2090-0155
DOI
10.1155/2024/3790617
