Review Article
Artificial Intelligence and Deep Learning Assisted Rapid Diagnosis of COVID-19 from Chest Radiographical Images: A Survey
Table 2
Performance analysis of several deep learning models for classification of COVID-19 from X-ray images.
| Researchers | Deep learning model | Performance |
| Zhang et al. [45] | Novel | 96% accuracy | Panwar et al. [46] | nCOVnet | 97.62% accuracy | Stephen et al. [48] | Novel | 95% accuracy | Wang and Wong [49] | Novel | 83.5% accuracy | Ghosal and Tucker [57] | ResNet50V2 | | Toğaçar et al. [54] | MobileNetV2 and SqueezeNet | 99.27% accuracy | Ozturk et al. [56] | DarkCovidNet (based on YOLO) | 98.08% accuracy | Narin et al. [58] | ResNet50 [23], InceptionV3, and InceptionResNetV2 | 98% accuracy using ResNet50 | Hemdan et al. [62] | VGG19, DenseNet201, MobileNetV2, Xception, ResNetV2, InceptionV3, and InceptionResNetV2 | 90% accuracy using VGG19 | Sethy et al. [61] | Thirteen CNN models | 95.38% accuracy using RestNet50 | Khan et al. [64] | CoroNet (based on Xception) | 89.6% accuracy | Rahimzadeh and Attar [65] | The concatenated network of Xception and ResNet50V2 | 99.50% accuracy | Chowdhury et al. [66] | DenseNet201, ResNet18, AlexNet, and SqueezeNet | 98.3% accuracy using SqueezeNet | Apostolopoulos et al. [68] | VGG19, MobileNetV2, InceptionResNetV2, Inception, and Xception | 98.75% accuracy using VGG19 | Altan and Karasu [69] | EfficientNetB0 | 99.69% accuracy | Brunese et al. [70] | Transfer learning | 97% accuracy | Ucar and Korkmaz [71] | Deep-SqueezeNet (based on SqueezeNet) | 98.26% accuracy | Mahmud et al. [72] | CovXNet | 97.4% accuracy |
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