https://scholars.lib.ntu.edu.tw/handle/123456789/628909
Title: | An improved 3-D attention CNN with hybrid loss and feature fusion for pulmonary nodule classification | Authors: | Huang, Yao-Sian Wang, Teh-Chen Huang, Sheng-Zhi Zhang, Jun HSIN-MING CHEN YEUN-CHUNG CHANG RUEY-FENG CHANG |
Keywords: | Attention mechanism; Computer-aided diagnosis; Feature pyramid network; Hybrid loss; Lung nodules; Residual network | Issue Date: | Feb-2023 | Publisher: | ELSEVIER IRELAND LTD | Journal Volume: | 229 | Source: | Computer methods and programs in biomedicine | Abstract: | Lung cancer has the highest cancer-related mortality worldwide, and lung nodule usually presents with no symptom. Low-dose computed tomography (LDCT) was an important tool for lung cancer detection and diagnosis. It provided a complete three-dimensional (3-D) chest image with a high resolution.Recently, convolutional neural network (CNN) had flourished and been proven the CNN-based computer-aided diagnosis (CADx) system could extract the features and help radiologists to make a preliminary diagnosis. Therefore, a 3-D ResNeXt-based CADx system was proposed to assist radiologists for diagnosis in this study. |
URI: | https://scholars.lib.ntu.edu.tw/handle/123456789/628909 | ISSN: | 0169-2607 | DOI: | 10.1016/j.cmpb.2022.107278 |
Appears in Collections: | 醫學院附設醫院 (臺大醫院) |
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