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Updated: Aug 12, 2025
Author Spotlight: A 3D Digital Model for the Diagnosis and Treatment of Pulmonary Nodules
Published on: May 19, 2023
An attention-based deep learning network for lung nodule malignancy discrimination
1Department of Interventional Radiology, Qinghai Red Cross Hospital, Xining, Qinghai, China.
Introduction:
Effective classification of lung cancers plays a vital role in lung tumor diagnosis and subsequent treatments. However, classification of benign and malignant lung nodules remains inaccurate.
Methods:
This study proposes a novel multimodal attention-based 3D convolutional neural network (CNN) which combines computed tomography (CT) imaging features and clinical information to classify benign and malignant nodules.
Results:
An average diagnostic sensitivity of 96.2% for malignant nodules and an average accuracy of 81.6% for classification of benign and malignant nodules were achieved in our algorithm, exceeding results achieved from traditional ResNet network (sensitivity of 89% and accuracy of 80%) and VGG network (sensitivity of 78% and accuracy of 73.1%).
Discussion:
The proposed deep learning (DL) model could effectively distinguish benign and malignant nodules with higher precision.
