2023
DOI: 10.3390/electronics12102333
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3DAGNet: 3D Deep Attention and Global Search Network for Pulmonary Nodule Detection

Abstract: In traditional clinical medicine, respiratory physicians or radiologists often identify the location of lung nodules by highlighting targets in consecutive CT slices, which is labor-intensive and easy-to-misdiagnose work. To achieve intelligent detection and diagnosis of CT lung nodules, we designed a 3D convolutional neural network, called 3DAGNet, for pulmonary nodule detection. Inspired by the diagnostic process of lung nodule localization by physicians, the 3DGNet includes a spatial attention and a global … Show more

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Cited by 11 publications
(1 citation statement)
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“…In terms of SSC estimation, the classifier achieved a coefficient of determination (R 2 ) of 0.901, and for firmness estimation, the classifier achieved an R 2 of 0.532. Other recent works have proposed the use of CNN networks using different types of sensors adapted to the specific needs of each application, such as computed tomography [16], Doppler radar [17], and EEG signals [18].…”
Section: Introductionmentioning
confidence: 99%
“…In terms of SSC estimation, the classifier achieved a coefficient of determination (R 2 ) of 0.901, and for firmness estimation, the classifier achieved an R 2 of 0.532. Other recent works have proposed the use of CNN networks using different types of sensors adapted to the specific needs of each application, such as computed tomography [16], Doppler radar [17], and EEG signals [18].…”
Section: Introductionmentioning
confidence: 99%