Abstract
Lung cancer is one of the most common cancers in the world, and the detection and classification of benign-malignant lung nodules are critical during the diagnosis and treatment for lung cancer. In this paper, a multi-view improved dense convolutional network is proposed for the classification of benign-malignant pulmonary nodules, where more information of input multi-scale features can be extracted from 2D views of nine different directions. The improved dense block and other layers are linked by shortcuts, which optimizes the feature extraction. The proposed network model is trained in the LIDC-IDRI dataset, and the results show that the average classification accuracy and AUC are 86.52% and 97.23% respectively, which means that the network model has significantly improved the performance of benign-malignant pulmonary nodules classification.
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Shen, LH., Wang, XH., Gao, MX., Li, B. (2021). Classification of Benign-Malignant Pulmonary Nodules Based on Multi-view Improved Dense Network. In: Huang, DS., Jo, KH., Li, J., Gribova, V., Bevilacqua, V. (eds) Intelligent Computing Theories and Application. ICIC 2021. Lecture Notes in Computer Science(), vol 12836. Springer, Cham. https://doi.org/10.1007/978-3-030-84522-3_48
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