基于改进型 MobileNet 模型的 CT 图像中蜂巢肺的识别。

Recognition of honeycomb lung in CT images based on improved MobileNet model.

机构信息

College of Software, Taiyuan University of Technology, Taiyuan, China.

College of Information and Computer, Taiyuan University of Technology, Taiyuan, China.

出版信息

Med Phys. 2021 Aug;48(8):4304-4315. doi: 10.1002/mp.14873. Epub 2021 Jul 17.

DOI:10.1002/mp.14873

PMID:33826769

Abstract

PURPOSE

The research is to improve the efficiency and accuracy of recognition of honeycomb lung in CT images.

METHODS

Deep learning methods are used to achieve automatic recognition of honeycomb lung in CT images, however, are time consuming and less accurate due to the large amount of structural parameters. In this paper, a novel recognition method based on MobileNetV1 network, multiscale feature fusion method (MSFF), and dilated convolution is explored to deal with honeycomb lung in CT image classification. Firstly, the dilated convolution with different dilated rate is used to extract features to obtain receptive fields of different sizes, and then fuse the features of different scales at multiscale feature fusion block is used to solve the problem of feature loss and incomplete feature extraction. After that, by using linear activation functions (Sigmoid) instead of nonlinear activation functions (ReLu) in the improved deep separable convolution blocks to retain the feature information of each channel. Finally, by reducing the number of improved deep separable blocks to reduce the computation and resource consumption of the model.

RESULTS

The experimental results show that improved MobileNet model has the best performance and the potential for recognition of honeycomb lung image datasets, which includes 6318 images. By comparing with 4 traditional models (SVM, RF, decision tree, and KNN) and 11 deep learning models (LeNet-5, AlexNet, VGG-16, GoogleNet, ResNet18, DenseNet121, SENet18, InceptionV3, InceptionV4, Xception, and MobileNetV1), our model achieved the performance with an accuracy of 99.52%, a sensitivity of 99.35%, and a specificity of 99.89%.

CONCLUSION

Improved MobileNet model is designed for the automatic recognition and classification of honeycomb lung in CT images. Through experiments comparative analysis of other models of machine learning and deep learning, it is proved that the proposed improved MobileNet method has the best recognition accuracy with fewer the model parameters and less the calculation time.

摘要

目的

研究旨在提高 CT 图像中蜂窝肺识别的效率和准确性。

方法

深度学习方法被用于实现 CT 图像中蜂窝肺的自动识别，然而，由于结构参数较多，因此耗时且准确性较低。在本文中，探索了一种基于 MobileNetV1 网络、多尺度特征融合方法（MSFF）和扩张卷积的新型识别方法，用于处理 CT 图像分类中的蜂窝肺。首先，使用不同扩张率的扩张卷积来提取特征，以获得不同大小的感受野，然后在多尺度特征融合块中融合不同尺度的特征，以解决特征丢失和特征提取不完整的问题。之后，通过在改进的深度可分离卷积块中使用线性激活函数（Sigmoid）代替非线性激活函数（ReLU），保留每个通道的特征信息。最后，通过减少改进的深度可分离块的数量来减少模型的计算和资源消耗。

结果

实验结果表明，改进的 MobileNet 模型在包括 6318 张图像的蜂窝肺图像数据集的识别方面具有最佳性能和潜力。通过与 4 种传统模型（SVM、RF、决策树和 KNN）和 11 种深度学习模型（LeNet-5、AlexNet、VGG-16、GoogleNet、ResNet18、DenseNet121、SENet18、InceptionV3、InceptionV4、Xception 和 MobileNetV1）进行比较，我们的模型在准确性、敏感性和特异性方面分别达到了 99.52%、99.35%和 99.89%。