COVID-19 Recognition Using Self-Supervised Learning Approach in Three New Computed Tomography Databases

Amgad Muneer, Rao Faizan Ali, Suliman Mohamed Fati, Sheraz Naseer

Abstract

Pretraining has been a widely used approach in computer vision and natural language processing, as it typically results in significant performance improvements. The most commonly used pretraining method is transfer learning (TL), which uses labeled data to develop a suitable representation network. Recently, a novel technique for pretraining, self-supervised learning (SSL), has shown promise in various applications. Computed Tomography (CT) is a convenient way to diagnose COVID-19 patients during the COVID-19 pandemic phase. However, openly accessible COVID-19 datasets are highly problematic to gain, which hampers the investigation, and the growth of AI-powered diagnosis approaches of COVID-19 depending on CTs. By using an open-source dataset COVID-CT solve this problem, comprising of 349 COVID-19 CT images. This paper uses three CT datasets to introduce a diagnostic approach based on contrastive self-supervised learning (CSSL) and TL. The effectiveness of CSSL demonstrates an improvement in learning representations. Using multi-task learning and leveraging CSSL pretraining by incorporating lung masks and lesion masks, the authors achieved an accuracy of 0.89 with an F1 of 0.90 and AUC of 0.98.

Keywords: transfer learning, self-supervised learning, COVID-19, computed tomography, lung mask, lesion mask.


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References


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