  With COVID-19 cases rising rapidly, deep learning has emerged as a promising
diagnosis technique. However, identifying the most accurate models to
characterize COVID-19 patients is challenging because comparing results
obtained with different types of data and acquisition processes is non-trivial.
In this paper we designed, evaluated, and compared the performance of 20
convolutional neutral networks in classifying patients as COVID-19 positive,
healthy, or suffering from other pulmonary lung infections based on Chest CT
scans, serving as the first to consider the EfficientNet family for COVID-19
diagnosis and employ intermediate activation maps for visualizing model
performance. All models are trained and evaluated in Python using 4173 Chest CT
images from the dataset entitled "A COVID multiclass dataset of CT scans," with
2168, 758, and 1247 images of patients that are COVID-19 positive, healthy, or
suffering from other pulmonary infections, respectively. EfficientNet-B5 was
identified as the best model with an F1 score of 0.9769+/-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
0.9599+/-0.0251, sensitivity of 0.9682+/-0.0099, specificity of
0.9883+/-0.0150, and precision of 0.9526 +/- 0.0523. Intermediate activation
maps and Gradient-weighted Class Activation Mappings offered
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. With a prediction speed of
under 0.1 seconds on GPUs and 0.5 seconds on CPUs, our proposed model offers a
rapid, scalable, and accurate diagnostic for COVID-19.
