An improved 3-D attention CNN with hybrid loss and feature fusion for pulmonary nodule classification
Journal
Computer methods and programs in biomedicine
Journal Volume
229
Date Issued
2023-02
Author(s)
Huang, Yao-Sian
Wang, Teh-Chen
Huang, Sheng-Zhi
Zhang, Jun
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.
Subjects
Attention mechanism; Computer-aided diagnosis; Feature pyramid network; Hybrid loss; Lung nodules; Residual network
SDGs
Publisher
ELSEVIER IRELAND LTD
Type
journal article