Feng Yibo, Qiu Dawei, Cao Hui, Zhang Junzhong, Xin Zaihai, Liu Jing
College of Intelligence and Information Engineering, Shandong University of Traditional Chinese Medicine, Jinan 250355, P.R.China.
Sheng Wu Yi Xue Gong Cheng Xue Za Zhi. 2020 Aug 25;37(4):557-565. doi: 10.7507/1001-5515.202005056.
Coronavirus disease 2019 (COVID-19) has spread rapidly around the world. In order to diagnose COVID-19 more quickly, in this paper, a depthwise separable DenseNet was proposed. The paper constructed a deep learning model with 2 905 chest X-ray images as experimental dataset. In order to enhance the contrast, the contrast limited adaptive histogram equalization (CLAHE) algorithm was used to preprocess the X-ray image before network training, then the images were put into the training network and the parameters of the network were adjusted to the optimal. Meanwhile, Leaky ReLU was selected as the activation function. VGG16, ResNet18, ResNet34, DenseNet121 and SDenseNet models were used to compare with the model proposed in this paper. Compared with ResNet34, the proposed classification model of pneumonia had improved 2.0%, 2.3% and 1.5% in accuracy, sensitivity and specificity respectively. Compared with the SDenseNet network without depthwise separable convolution, number of parameters of the proposed model was reduced by 43.9%, but the classification effect did not decrease. It can be found that the proposed DWSDenseNet has a good classification effect on the COVID-19 chest X-ray images dataset. Under the condition of ensuring the accuracy as much as possible, the depthwise separable convolution can effectively reduce number of parameters of the model.
2019冠状病毒病（COVID-19）已在全球迅速传播。为了更快地诊断COVID-19，本文提出了一种深度可分离密集网络。该论文构建了一个以2905张胸部X光图像为实验数据集的深度学习模型。为了增强对比度，在网络训练前使用对比度受限自适应直方图均衡化（CLAHE）算法对X光图像进行预处理，然后将图像放入训练网络并将网络参数调整到最优。同时，选择Leaky ReLU作为激活函数。使用VGG16、ResNet18、ResNet34、DenseNet121和SDenseNet模型与本文提出的模型进行比较。与ResNet34相比，本文提出的肺炎分类模型在准确率、灵敏度和特异性上分别提高了2.0%、2.3%和1.5%。与没有深度可分离卷积的SDenseNet网络相比，本文提出模型的参数数量减少了43.9%，但分类效果并未下降。可以发现，本文提出的DWSDenseNet对COVID-19胸部X光图像数据集具有良好的分类效果。在尽可能保证准确率的情况下，深度可分离卷积能够有效减少模型的参数数量。
