Deep learning technology has been successfully used in Chest X-ray (CXR) images of COVID-19 patients. However, due to the characteristics of COVID-19 pneumonia and X-ray imaging, the deep learning methods still face many challenges, such as lower imaging quality, fewer training samples, complex radiological features and irregular shapes. To address these challenges, this study first introduces an extensive NSNP-like neuron model, and then proposes a multitask adversarial network architecture based on ENSNP-like neurons for chest X-ray images of COVID-19, called MAE-Net. The MAE-Net serves two tasks: (i) converting low-quality CXR images to high-quality images; (ii) classifying CXR images of COVID-19. The adversarial architecture of MAE-Net uses two generators and two discriminators, and two new loss functions have been introduced to guide the optimization of the network. The MAE-Net is tested on four benchmark COVID-19 CXR image datasets and compared them with eight deep learning models. The experimental results show that the proposed MAE-Net can enhance the conversion quality and the accuracy of image classification results.

深度学习技术已成功应用于 COVID-19 患者的胸部 X 光 (CXR) 图像。然而，由于COVID-19肺炎和X射线成像的特点，深度学习方法仍然面临成像质量较低、训练样本较少、放射学特征复杂和形状不规则等诸多挑战。为了应对这些挑战，本研究首先引入了一种广泛的类 NSNP 神经元模型，然后针对 COVID-19 的胸部 X 射线图像提出了一种基于类 ENSNP 神经元的多任务对抗网络架构，称为 MAE-Net。 MAE-Net 有两个任务：（i）将低质量 CXR 图像转换为高质量图像； (ii) 对 COVID-19 的 CXR 图像进行分类。 MAE-Net的对抗架构使用两个生成器和两个判别器，并引入了两个新的损失函数来指导网络的优化。 MAE-Net 在四个基准 COVID-19 CXR 图像数据集上进行了测试，并将其与八个深度学习模型进行了比较。实验结果表明，所提出的MAE-Net可以提高转换质量和图像分类结果的准确性。