In Frontiers in neuroscience ; h5-index 72.0
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.
Liu Gang, Liu Fei, Gu Jun, Mao Xu, Xie XiaoTing, Sang Jingyao
2022
artificial intelligence, attention mechanism gate module, lung nodules, malignancy, multimodal
