The COVID-19 outbreak has catastrophically affected both public health system and world economy. Swift diagnosis of the positive cases will help in providing proper medical attention to the infected individuals and will also aid in effective tracing of their contacts to break the chain of transmission. Blending Artificial Intelligence (AI) with chest X-ray images and incorporating these models in a smartphone can be handy for the accelerated diagnosis of COVID-19. In this study, publicly available datasets of chest X-ray images have been utilized for training and testing of five pre-trained Convolutional Neural Network (CNN) models namely VGG16, MobileNetV2, Xception, NASNetMobile and InceptionResNetV2. Prior to the training of the selected models, the number of images in COVID-19 category has been increased employing traditional augmentation and Generative Adversarial Network (GAN). The performance of the five pre-trained CNN models utilizing the images generated with the two strategies has been compared. In the case of models trained using augmented images, Xception (98%) and MobileNetV2 (97.9%) turned out to be the ones with highest validation accuracy. Xception (98.1%) and VGG16 (98.6%) emerged as models with the highest validation accuracy in the models trained with synthetic GAN images. The best performing models have been further deployed in a smartphone and evaluated. The overall results suggest that VGG16 and Xception, trained with the synthetic images created using GAN, performed better compared to models trained with augmented images. Among these two models VGG16 produced an encouraging Diagnostic Odd Ratio (DOR) with higher positive likelihood and lower negative likelihood for the prediction of COVID-19.

COVID-19 疫情对公共卫生系统和世界经济造成了灾难性影响。对阳性病例的迅速诊断将有助于为受感染者提供适当的医疗护理，也将有助于有效追踪他们的接触者以打破传播链。将人工智能 (AI) 与胸部 X 光图像相结合，并将这些模型整合到智能手机中，可以方便地加速 COVID-19 的诊断。在这项研究中，公开可用的胸部 X 光图像数据集已用于训练和测试五个预训练的卷积神经网络 (CNN) 模型，即 VGG16、MobileNetV2、Xception、NASNetMobile 和 InceptionResNetV2。在训练所选模型之前，使用传统增强和生成对抗网络 (GAN) 增加了 COVID-19 类别中的图像数量。比较了使用这两种策略生成的图像的五个预训练 CNN 模型的性能。对于使用增强图像训练的模型，Xception (98%) 和 MobileNetV2 (97.9%) 结果证明是验证准确率最高的模型。Xception (98.1%) 和 VGG16 (98.6%) 成为使用合成 GAN 图像训练的模型中验证准确率最高的模型。性能最佳的模型已进一步部署在智能手机中并进行了评估。总体结果表明，与使用增强图像训练的模型相比，使用 GAN 创建的合成图像训练的 VGG16 和 Xception 表现更好。