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| A survey of depth learning methods for detecting lung nodules by CT images |
| HU Weijian1, CHEN Wei2, FENG Haozhe2, ZHANG Tianping2, ZHU Zhengmao2, PAN Qiaoming1 |
1. Engineering College, Lishui University, Lishui 323000, Zhejiang Province, China;
2. State Key Lab of CAD & CG, College of Computer Science, Zhejiang University, Hangzhou 310058, China |
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Abstract Lung cancer is one of the most aggressive cancers and detecting lung nodule by CT images at the early stage is of vital importance to treating lung cancer. This paper overviews the application of a revolutionary image recognition method, deep learning, in the detection of lung nodule. First, we contrast different convolutional neural network (CNN) architectures and their performance in image recognition. Then, we mainly focus on various deep learning methods including faster-RCNN, transfer learning, residual network and curriculum learning to train the classifier. We also introduce some available databases of lung CT images in the last section of our paper.
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Received: 23 May 2017
Published: 09 December 2017
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Cite this article:
HU Weijian, CHEN Wei, FENG Haozhe, ZHANG Tianping, ZHU Zhengmao, PAN Qiaoming. A survey of depth learning methods for detecting lung nodules by CT images. Journal of Zhejiang University (Science Edition), 2017, 44(4): 379-384.
URL:
https://www.zjujournals.com/sci/EN/Y2017/V44/I4/379
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应用于平扫CT图像肺结节检测的深度学习方法综述
肺癌是一种致死率很高的癌症.通过肺部平扫CT影像检测肺结节对肺癌早期诊断、治疗意义重大.全面介绍了一种革命性的图像识别技术——深度学习方法，在肺结节检测中的应用.首先，横向对比了不同卷积神经网络的结构及其在图像识别上的效果，其次着重分析了不同深度学习方法在训练肺结节分类器上的应用，包括faster-RCNN、迁移学习、残差学习以及迁移学习.还介绍了一些可用的肺部CT影像数据集供读者参考.
关键词：
深度学习,
肺结节,
卷积神经网络,
数据库
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