6 March 2018 Preliminary study of benign and malignant differentiation of small pulmonary nodules in lung CT images by using deep learning convolutional neural network
Author Affiliations +
Abstract
The benign and malignant differential diagnosis of small pulmonary nodules (diameter < 20 mm) found in lung CT images is big challenges for most of radiologists. Here, we presented our preliminary study of benign and malignant differentiation of small pulmonary nodules in lung CT images by using deep learning Convolutional Neural Network (CNN). The 921 cases with small benign and malignant pulmonary nodules confirmed by pathology were collected from three data sources and were used to train and validate the CNN. The preliminary results of AUCs of ROC curves for differentiating benign and malignant pulmonary small nodules with various types and sizes of solid, semi-solid and ground glass nodules were presented and discussed.
© (2018) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Yangfan Ni, Yangfan Ni, Haozhe Huang, Haozhe Huang, Dezhong Zheng, Dezhong Zheng, Jianyong Sun, Jianyong Sun, Yuanyuan Yang, Yuanyuan Yang, Mingqing Wang, Mingqing Wang, Yiping Gu, Yiping Gu, Ming Li, Ming Li, Guozhen Zhang, Guozhen Zhang, Wentao Li, Wentao Li, Jianguo Zhang, Jianguo Zhang, } "Preliminary study of benign and malignant differentiation of small pulmonary nodules in lung CT images by using deep learning convolutional neural network", Proc. SPIE 10579, Medical Imaging 2018: Imaging Informatics for Healthcare, Research, and Applications, 1057916 (6 March 2018); doi: 10.1117/12.2294483; https://doi.org/10.1117/12.2294483
PROCEEDINGS
11 PAGES


SHARE
Back to Top