Preliminary study of benign and malignant differentiation of small pulmonary nodules in lung CT images by using deep learning convolutional neural network

Yangfan Ni; Haozhe Huang; Jianyong Sun; Yuanyuan Yang; Mingqing Wang; Yiping Gu; Ming Li; Guozhen Zhang; Wentao Li; Jianguo Zhang

doi:10.1117/12.2294483

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

Yangfan Ni, Haozhe Huang, Jianyong Sun, Yuanyuan Yang, Mingqing Wang, Yiping Gu, Ming Li, Guozhen Zhang, Wentao Li, Jianguo Zhang

Author Affiliations +

Proceedings Volume 10579, Medical Imaging 2018: Imaging Informatics for Healthcare, Research, and Applications; 1057916 (2018) https://doi.org/10.1117/12.2294483
Event: SPIE Medical Imaging, 2018, Houston, Texas, United States

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.

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Yangfan Ni, Haozhe Huang, Jianyong Sun, Yuanyuan Yang, Mingqing Wang, Yiping Gu, Ming Li, Guozhen Zhang, Wentao Li, and 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); https://doi.org/10.1117/12.2294483

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