[1]陈强锐,谢世朋.基于深度学习的肺部肿瘤检测方法[J].计算机技术与发展,2018,28(04):201-204.[doi:10.3969/ j. issn.1673-629X.2018.04.043]
　CHEN Qiang-rui,XIE Shi-peng.Lung Cancer Detection Method Based on Deep Learning[J].,2018,28(04):201-204.[doi:10.3969/ j. issn.1673-629X.2018.04.043]
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基于深度学习的肺部肿瘤检测方法()
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《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
28
期数:
2018年04期
页码:
201-204
栏目:
安全与防范
出版日期:
2018-04-10

文章信息/Info

Title:
Lung Cancer Detection Method Based on Deep Learning
文章编号:
1673-629X(2018)04-0201-04
作者:
陈强锐; 谢世朋
南京邮电大学 通信与信息工程学院,江苏 南京 210003
Author(s):
CHEN Qiang-rui; XIE Shi-peng
School of Communication and Information Engineering,Nanjing University of Posts and Telecommunications,Nanjing 210003,China
关键词:
深度学习; 肺部肿瘤检测; 特征提取; 卷积神经网络; 区域建议网络
Keywords:
deep learning; lung tumor detection; feature extraction; convolutional neural network; region proposal network
分类号:
TP391
DOI:
10.3969/ j. issn.1673-629X.2018.04.043
文献标志码:
A
摘要:
随着现代计算机技术的发展与应用,计算机辅助诊断系统在医学影像分析领域的地位变得愈发重要。 其技术的关键在于病灶的定位与分类。 由于图像的特征提取十分复杂,若应用传统机器学习方法,则需对图像作大量的预处理。文中提出一种基于深度学习的肺部肿瘤检测方法,运用卷积神经网络对患者肺部肿瘤图像进行特征提取。 结合区域建议网络预测肿瘤在图片中可能存在的位置,同时生成建议框。 利用学习好的特征对目标区域进行分类并微调建议框的位置。 该方法无需人工设计目标特征,通过卷积神经网络学习到的特征更加具有代表性,且能够较好地预测肿瘤的位置。在 NLST 以及 Kaggle 的数据集上对该方法进行了评估。 实验结果表明,该方法具有较高的准确率和效率。
Abstract:
With the development and application of modern computer technology,computer aided diagnosis system plays an important role in the field of medical image analysis. The key to this technology is the location and classification of lesions. As the feature extraction of the image is very complex,many image preprocessing methods must be used when the traditional machine learning method as a solution.We put forward a method for the detection of lung cancer based on deep learning. The images of lung tumors， features are extracted by convolutional neural network,and the locations in the picture where tumors may exist are predicted in combination with region proposal network,generating the proposal boxes simultaneously. The target area is classified by learned feature maps and the positions of the proposal boxes will be fine-tuned. The proposed method does not need to design the features manually,and the features learned by the convolutional neural network are more representative,which also could predict the location of the tumor. The experiments of evaluation on NLST and Kaggle data sets show that it has high accuracy and efficiency.

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更新日期/Last Update: 2018-06-08