激光与光电子学进展, 2020, 57 (4): 041013, 网络出版: 2020-02-20   

C-3D可变形卷积神经网络模型的肺结节检测 下载: 1096次

Detection of Pulmonary Nodules Based on C-3D Deformable Convolutional Neural Network Model
作者单位
1 上海海洋大学工程学院, 上海 210306
2 上海建桥学院机电学院, 上海 210306
3 上海市第六人民医院东院放射介入科, 上海 210306
摘要
在C-3D卷积神经网络模型基础上,提出了一种三维可变形卷积神经网络以实现肺结节的检测。在模型的主体结构上,采用了三维可变形卷积和三维可变形池化的操作,解决了传统的方块卷积与池化在应对不规则的肺结节时,无法高效率地收集到肺结节像素点的问题。在模型的输入上,通过调整三维卷积神经网络的输入,实现了卷积神经网络对样本图片的32×32×32像素逐步扫描和识别,在扫描识别的同时进行定位,解决了肺结节定位问题。在模型的输出上,借鉴了全卷积神经网络的思想,将C-3D网络的第一层全连接层改为卷积层,解决训练时内存会溢出的问题。在模型参数上,提出了三种不同学习率和三种优化函数进行精确的实验对比,绘制了不同学习率和优化函数的参数对比图,根据实验结果找到最优的卷积神经网络模型学习率和优化函数参数。对实验结果的分析表明,该方法在受试者工作曲线下面积、分类准确率、召回率、F1值均取得了显著的提高。
Abstract
A three-dimensional (3D) deformable convolutional neural network is proposed based on the C-3D convolutional neural network for realizing detection of pulmonary nodules. A 3D deformable convolution and pooling is used in the main structure of the model. It solves the problem that the traditional square convolution and pooling cannot collect the pixels of pulmonary nodules efficiently when dealing with irregular pulmonary nodules. By adjusting the input of the 3D convolutional neural network, the scanning and recognition of 32×32×32 pixels of a sample image are realized step by step by using a convolutional neural network, thereby realizing pulmonary nodule localization. As for the output of the model, the first full connection layer of the C-3D network is replaced by the convolution layer based on a full convolution neural network, to solve the problem of memory overflow during training. In terms of model parameters, three different learning rates and optimization functions are designed for experimental comparison, and the parametric comparison diagrams of three different learning rates and optimization functions are drawn. According to the experimental results, the optimal learning rate and parameters of optimization functions of the convolutional neural network are selected. The experimental results show that the area under the receiver operating curve, classification accuracy, recall, and F1 value of the proposed method have been significantly improved.

阮宏洋, 陈志澜, 程英升, 杨凯. C-3D可变形卷积神经网络模型的肺结节检测[J]. 激光与光电子学进展, 2020, 57(4): 041013. Hongyang Ruan, Zhilan Chen, Yingsheng Cheng, Kai Yang. Detection of Pulmonary Nodules Based on C-3D Deformable Convolutional Neural Network Model[J]. Laser & Optoelectronics Progress, 2020, 57(4): 041013.

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