The feasibility of transfer learning for differentiation H1N1 Influenza from COVID-19 on chest CT

Abstract

Objectives: It is unlikely that by fall and winter of 2020, standard vaccine or treatment is available for COVID-19 infection. In this period, differentiation between COVID-19 and Influenza induced pneumonia will be critical for patient management. To develop an automated platform to perform this task, artificial intelligence models were developed by using the transfer learning techniques on chest CT.Methods: Chest CT images from known cases of COVID-19, H1N1 Influenza induced pneumonia (before December 2019), and normal chest CTs were collected. Different pre-trained Convolutional Neural Networks (CNN) models, including VGG 16, VGG 19, ResNet-50, Wide ResNet, InceptionV3, and SqueezNet were fine-tuned on this data set. 60% of the dataset was used for training, 20% for validation, and 20% for test the final models. Accuracy, Precision, Recall and F1 score of each model were calculated.Results: For differentiation of COVID-19 pneumonia versus H1N1 Influenza pneumonia versus normal CTs, the ResNet-50 (accuracy above 92%) outperformed other models followed by InceptionV3 and wide ResNet.Conclusions: The pre-trained image classification AI models are feasible to be fine-tuned and used for differentiation COVID-19 versus H1N1 Influenza pneumonia. In this context, ResNet-50 and then InceptionV3 architectures appear more promising and are suitable start points for further development. We share the source code and trained models in the supplement of this manuscript to be used by other researchers for further development.