An attention-based deep learning network for lung nodule malignancy discrimination - Liu, Gang; Liu, Fei; Gu, Jun; Mao, Xu; Xie, XiaoTing; Sang, Jingyao (Frontiers in Neuroscience, 2023)
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. This study proposes a novel multimodal attention-based 3D convolutional neural network which combines CT imaging features and clinical information to classify benign and malignant nodules. 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%). The proposed deep learning model could effectively distinguish benign and malignant nodules with higher precision.