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The rapid and seemingly endless expansion of COVID-19 can be traced back tothe inefficiency and shortage of testing kits that offer accurate results in atimely manner. An emerging popular technique, which adopts improvements made inmobile ultrasound technology, allows for healthcare professionals to conductrapid screenings on a large scale. We present an image-based solution that aimsat automating the testing process which allows for rapid mass testing to beconducted with or without a trained medical professional that can be applied torural environments and third world countries. Our contributions towards rapidlarge-scale testing include a novel deep learning architecture capable ofanalyzing ultrasound data that can run in real-time and significantly improvethe current state-of-the-art detection accuracies using image-based COVID-19detection.

POCFormer: A Lightweight Transformer Architecture for Detection of COVID-19 Using Point of Care Ultrasound