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• W3088894547 abstract "Lung cancer is one of the most deadly diseases in the world. Detecting such tumors at an early stage can be a tedious task. Existing deep learning architecture for lung nodule identification used complex architecture with large number of parameters. This study developed a cascaded architecture which can accurately segment and classify the benign or malignant lung nodules on computed tomography (CT) images. The main contribution of this study is to introduce a segmentation network where the first stage trained on a public data set can help to recognize the images which included a nodule from any data set by means of transfer learning. And the segmentation of a nodule improves the second stage to classify the nodules into benign and malignant. The proposed architecture outperformed the conventional methods with an area under curve value of 95.67%. The experimental results showed that the classification accuracy of 97.96% of our proposed architecture outperformed other simple and complex architectures in classifying lung nodules for lung cancer detection." @default.
• W3088894547 created "2020-10-01" @default.
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• W3088894547 date "2020-09-24" @default.
• W3088894547 modified "2023-10-18" @default.
• W3088894547 title "Transfer Learning by Cascaded Network to identify and classify lung nodules for cancer detection" @default.
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• W3088894547 doi "https://doi.org/10.48550/arxiv.2009.11587" @default.
• W3088894547 hasPublicationYear "2020" @default.
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