Efficient Deep Learning Model for COVID-19 Detection in large CT images datasets: A cross-dataset analysis

Abstract

Abstract Early detection and diagnosis are critical factors to control the COVID-19 spreading. A number of deep learning-based methodologies have been recently proposed for COVID-19 screening in CT scans as a tool to automate and help with the diagnosis. To achieve these goals, in this work, we propose a slice voting-based approach extending the EfficientNet Family of deep artificial neural networks.We also design a specific data augmentation process and transfer learning for such task.Moreover, a cross-dataset study is performed into the two largest datasets to date. The proposed method presents comparable results to the state-of-the-art methods and the highest accuracy to date on both datasets (accuracy of 87.60\% for the COVID-CT dataset and accuracy of 98.99% for the SARS-CoV-2 CT-scan dataset). The cross-dataset analysis showed that the generalization power of deep learning models is far from acceptable for the task since accuracy drops from 87.68% to 56.16% on the best evaluation scenario.These results highlighted that the methods that aim at COVID-19 detection in CT-images have to improve significantly to be considered as a clinical option and larger and more diverse datasets are needed to evaluate the methods in a realistic scenario.

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