摘要
为提高不易分割诊断的毛玻璃结节的分类准确率,同时针对VGG16网络结构卷积层数深,参数多的问题,提出一种基于灰度增强、纹理和形状滤波增强的三维深度卷积神经网络用于肺结节分类。对VGG16网络结构进行优化,提出的模型在肺结节公开数据集LIDC-IDRI上进行训练和测试。结果表明,采用灰度增强、纹理和形状滤波增强相结合的方法图像分类精度最高,准确率为91.7%,其他评价指标包括敏感性和特异性也略有提高,优于现有方法。
In order to improve the classification accuracy of ground glass nodules that are difficult to segment and diagnose and at the same time,the VGG16 network structure has deep convolutional layers and many parameters,A 3D deep convolutional neural network based on intensity,texture,and shape-enhanced images for pulmonary nodule recognition was proposed.The VGG16 network structure was optimized,and the proposed model was trained and tested on the public nodule dataset of lung nodules LIDC-IDRI.The results showed that the proposed method using the composition of intensity,texture and shape-enhanced has the highest image classification accuracy,with an accuracy of 91.7%.Other measures,including sensitivity and specificity,also improved slightly,It is superior to existing methods.
作者
王卫兵
王卓
徐倩
孙宏
WANG Wei-bing;WANG Zhuo;XU Qian;SUN Hong(School of Computer Science and Technology,Harbin University of Science and Technology,Harbin 150080,China;Distribution Room,Harbin Branch of Heilongjiang Power Corporation,Harbin 150080,China)
出处
《哈尔滨理工大学学报》
CAS
北大核心
2021年第4期87-93,共7页
Journal of Harbin University of Science and Technology
基金
国家自然科学基金(61305001).
关键词
肺结节
深度学习
卷积神经网络
pulmonary nodule
deep learning
convolutional neural network
