College of Electronic Information and Automation, Tianjin University of Science and Technology, Tianjin, 300222, China.
Sci Rep. 2023 Apr 1;13(1):5359. doi: 10.1038/s41598-023-32462-2.
Coronavirus 2019 (COVID-19) is a new acute respiratory disease that has spread rapidly throughout the world. This paper proposes a novel deep learning network based on ResNet-50 merged transformer named RMT-Net. On the backbone of ResNet-50, it uses Transformer to capture long-distance feature information, adopts convolutional neural networks and depth-wise convolution to obtain local features, reduce the computational cost and acceleration the detection process. The RMT-Net includes four stage blocks to realize the feature extraction of different receptive fields. In the first three stages, the global self-attention method is adopted to capture the important feature information and construct the relationship between tokens. In the fourth stage, the residual blocks are used to extract the details of feature. Finally, a global average pooling layer and a fully connected layer perform classification tasks. Training, verification and testing are carried out on self-built datasets. The RMT-Net model is compared with ResNet-50, VGGNet-16, i-CapsNet and MGMADS-3. The experimental results show that the RMT-Net model has a Test_ acc of 97.65% on the X-ray image dataset, 99.12% on the CT image dataset, which both higher than the other four models. The size of RMT-Net model is only 38.5 M, and the detection speed of X-ray image and CT image is 5.46 ms and 4.12 ms per image, respectively. It is proved that the model can detect and classify COVID-19 with higher accuracy and efficiency.
新型冠状病毒肺炎（COVID-19）是一种新出现的急性呼吸道传染病，已在全球迅速蔓延。本文提出了一种基于 ResNet-50 融合 Transformer 的新型深度学习网络，命名为 RMT-Net。它在 ResNet-50 的骨干网络上，利用 Transformer 捕捉远距离特征信息，采用卷积神经网络和深度卷积获取局部特征，降低计算成本，加速检测过程。RMT-Net 包括四个阶段的模块，以实现不同感受野的特征提取。在前三阶段，采用全局自注意力方法捕捉重要的特征信息，并构建令牌之间的关系。在第四阶段，采用残差块提取特征的细节。最后，通过全局平均池化层和全连接层进行分类任务。在自建数据集上进行训练、验证和测试。将 RMT-Net 模型与 ResNet-50、VGGNet-16、i-CapsNet 和 MGMADS-3 进行比较。实验结果表明，RMT-Net 模型在 X 射线图像数据集上的 Test_acc 为 97.65%，在 CT 图像数据集上的 Test_acc 为 99.12%，均高于其他四个模型。RMT-Net 模型的大小仅为 38.5M，X 射线图像和 CT 图像的检测速度分别为 5.46ms 和 4.12ms。证明该模型能够以更高的精度和效率检测和分类 COVID-19。
