AUTHOR=Liu Gang , Liu Fei , Gu Jun , Mao Xu , Xie XiaoTing , Sang Jingyao TITLE=An attention-based deep learning network for lung nodule malignancy discrimination JOURNAL=Frontiers in Neuroscience VOLUME=Volume 16 - 2022 YEAR=2023 URL=https://www.frontiersin.org/journals/neuroscience/articles/10.3389/fnins.2022.1106937 DOI=10.3389/fnins.2022.1106937 ISSN=1662-453X ABSTRACT=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.