CT影像识别的卷积神经网络模型
Journal of Computer-aided Design & Computer Graphics（2018）
Abstract
针对传统分类方法分割精度低、特征提取耗时等问题,构建一个适用于CT肺结节良恶性分类的卷积神经网络模型.首先确定网络深度、卷积核数目和卷积核大小等参数,构建卷积神经网络初始模型;然后选择激活函数类型、学习率和学习率衰减策略等训练参数;最后提出对感兴趣区域划分局部子区域的方式增强样本进行训练.在LIDC-IDRI数据集上进行实验的结果表明,准确率、特异性、敏感性及AUC值分别达到92.50%,0.91,0.94和0.93;对恶性结节的识别能力明显优于其他网络模型.
MoreKey words
ct images recognition,convolutional neural network model,neural network model,neural network
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