@inproceedings{2ccf284d0c734edc880aadfed635a0ea,
title = "Lung nodule detection based on 3D convolutional neural networks",
abstract = "With the increasing number of lung cancer patients, the CAD system is playing an increasingly important rule in the field of automatic identification for medical images. Since the 3D characteristics of low-dose CT images make the 3D convolution more suitable than 2D convolution, in this paper, we propose a method to detect lung nodule of lung CT images using 3D convolutional neural networks. Combined with the traditional morphological preprocessing methods, 3D convolutional neural networks are applied to lung CT images. The experimental accuracy indicates that this method is suitable for the problem of lung nodule detection and has great room to improve. The experimental results also demonstrate that the application of deep learning in the medical field will bring great progress for medical development.",
keywords = "3D convolutional network, CT lung image, deep learning, nodule detection",
author = "Lei Fan and Zhaoqiang Xia and Xiaobiao Zhang and Xiaoyi Feng",
note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 2017 International Conference on the Frontiers and Advances in Data Science, FADS 2017 ; Conference date: 23-10-2017 Through 25-10-2017",
year = "2017",
month = jul,
day = "1",
doi = "10.1109/FADS.2017.8253184",
language = "英语",
series = "Conference Proceedings - 2017 International Conference on the Frontiers and Advances in Data Science, FADS 2017",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "7--10",
booktitle = "Conference Proceedings - 2017 International Conference on the Frontiers and Advances in Data Science, FADS 2017",
}
