Understanding and classifying Chest X-Ray (CXR) and computerised tomography (CT) images are of great significance for COVID-19 diagnosis. The existing research on the classification for COVID-19 cases faces the challenges of data imbalance, insufficient generalisability, the lack of comparative study, etc. To address these problems, this paper proposes a type of modified MobileNet to classify COVID-19 CXR images and a modified ResNet architecture for CT image classification. In particular, a modification method of convolutional neural networks (CNN) is designed to solve the gradient vanishing problem and improve the classification performance through dynamically combining features in different layers of a CNN. The modified MobileNet is applied to the classification of COVID-19, Tuberculosis, viral pneumonia (with the exception of COVID-19), bacterial pneumonia and normal controls using CXR images. Also, the proposed modified ResNet is used for the classification of COVID-19, non-COVID-19 infections and normal controls using CT images. The results show that the proposed methods achieve 99.6% test accuracy on the five-category CXR image dataset and 99.3% test accuracy on the CT image dataset. Six advanced CNN architectures and two specific COVID-19 detection models, i.e., COVID-Net and COVIDNet-CT are used in comparative studies. Two benchmark datasets and a CXR image dataset which combines eight different CXR image sources are employed to evaluate the performance of the above models. The results show that the proposed methods outperform the comparative models in classification accuracy, sensitivity, and precision, which demonstrate their potential in computer-aided diagnosis for healthcare applications.

理解和分类胸部 X 光 (CXR) 和计算机断层扫描 (CT) 图像对于 COVID-19 诊断具有重要意义。现有对 COVID-19 病例分类的研究面临数据不平衡、泛化性不足、缺乏比较研究等挑战。为了解决这些问题，本文提出了一种改进的 MobileNet 来对 COVID-19 CXR 图像进行分类，用于 CT 图像分类的改进的 ResNet 架构。特别地，设计了一种卷积神经网络（CNN）的修改方法，通过动态组合CNN不同层中的特征来解决梯度消失问题并提高分类性能。修改后的MobileNet应用于COVID-19、结核病、病毒性肺炎（COVID-19除外）的分类，细菌性肺炎和使用 CXR 图像的正常对照。此外，拟议的修改后的 ResNet 用于使用 CT 图像对 COVID-19、非 COVID-19 感染和正常控制进行分类。结果表明，所提出的方法在五类 CXR 图像数据集上达到了 99.6% 的测试精度，在 CT 图像数据集上达到了 99.3% 的测试精度。在比较研究中使用了六种先进的 CNN 架构和两种特定的 COVID-19 检测模型，即 COVID-Net 和 COVIDNet-CT。使用两个基准数据集和一个结合了八个不同 CXR 图像源的 CXR 图像数据集来评估上述模型的性能。结果表明，所提出的方法在分类精度、灵敏度和精度方面优于比较模型，