摘要
针对在肺结节分类中容易产生过拟合的问题,提出一种基于同步深度监督的多尺度肺结节分类方法。解决梯度消失问题,最小化分类错误并实现同一框架中同步训练多尺度肺结节图像,提高肺结节分类精确度。改进经典的AlexNet网络,使其更适合肺结节图像分类;将同步深度监督(SDS)策略纳入到AlexNet架构中,向隐藏层提供集成的同步监督;通过多尺度空间金字塔策略提取多尺度肺结节图像特征。实验结果表明,该方法的准确性达到93.68%,且在准确性、敏感度、特异度、ROC曲线下面积值上均优于其他分类方法。
To solve the problem of over-fitting in the classification of lung nodules, We proposed a classification method of multi-scale lung nodules based on synchronized deep supervision. It solved the problem of gradient disappearance, minimized classification errors, and achieved simultaneous training of multi-scale pulmonary nodule images in the same framework. It improved the accuracy of pulmonary nodule classification. The classic AlexNet network was improved to make it more suitable for the classification of images of lung nodules. The synchronized deep supervision(SDS) strategy was integrated into AlexNet architecture to provide integrated synchronized supervision to the hidden layers. And the multi-scale spatial pyramid strategy was used to extract the features of multi-scale lung nodules. The experimental results show that the accuracy of this method is 93.68%. It is superior to other ones in terms of accuracy, sensitivity, specificity, and area under the ROC curve.
作者
张丽
强彦
张小龙
王三虎
Zhang Li;Qiang Yan;Zhang Xiaolong;Wang Sanhu(College of Computer Science and Technology, Taiyuan University of Technology, Taiyuan 030024, Shanxi, China;College of Information Science and Technology, Pennsylvania State University, University Park 16802, Pennsylvania, USA;Department of Computer Science and Technology,Lvliang University, Lvliang 033000, Shanxi, China)
出处
《计算机应用与软件》
北大核心
2019年第9期214-219,共6页
Computer Applications and Software
基金
国家自然科学基金项目(61572344)
虚拟现实技术与系统国家重点实验室开放基金项目(VRLAB2018A08)
山西省回国留学人员科研项目(2016-038)
关键词
同步深度监督
多尺度
卷积神经网络
特征提取
Synchronized deep supervision
Multi-scale
Convolutional neural network
Feature extraction
