Before revision:  In this paper , we evaluated and compared 40 different convolutional neural network architectures for COVID-19 diagnosis, serving as the first to consider the EfficientNet family for COVID-19 diagnosis . EfficientNet-B5 is identified as the best model with an accuracy of 0.9931 +/ -0.0021, F1 score of 0.9931 +/ -0.0020 , sensitivity of 0.9952 +/ -0.0020, and specificity of 0.9912 +/ -0.0048 . Intermediate activation maps and Gradient-weighted Class Activation Mappings offer human-interpretable evidence of the model's perception of ground-class opacities and consolidations, hinting towards a promising use-case of artificial intelligence-assisted radiology tools  .
After revision:  In this paper , we evaluated and compared 40 different convolutional neural network architectures for COVID-19 diagnosis, serving as the first to consider the EfficientNet family for COVID-19 diagnosis . EfficientNet-B5 is identified as the best model with an accuracy of 0.9931 +/ -0.0046, accuracy of 0.9759+/-0.0048, sensitivity of 0.9788+/-0.0055, specificity of 0.9730+/-0.0057, and precision of 0.9751 +/- 0.0051. On an alternate 2-class dataset, EfficientNetB5 obtained an accuracy of 0.9845+/-0.0109, F1 score of 0.9931 +/ -0.0020 , sensitivity of 0.9952 +/ -0.0020, and specificity of 0.9912 +/ -0.0048 . Intermediate activation maps and Gradient-weighted Class Activation Mappings offer human-interpretable evidence of the model's perception of ground-class opacities and consolidations, hinting towards a promising use-case of artificial intelligence-assisted radiology tools .