Covid4HPC: A Fast and Accurate Solution for Covid Detection in the Cloud Using X-Rays

Danopoulos, D.; Kachris, C.; Soudris, D.

Covid-19 pandemic has devastated social life and damaged the economy of the global population with a constantly increasing number of cases and fatalities each day. A popular and cheap screening method is through chest X-Rays, however it is impossible for every patient with respiratory illness to be tested fast and get quarantined in time. Thus, an automatic approach is needed which is motivated by the efforts of the research community. Specifically, we introduce a Deep Neural Network topology that can classify chest X-Ray images from patients in 3 classes;Covid-19, Viral Pneumonia and Normal. Detecting COVID-19 infections on X-Rays with high accuracy is crucial and can aid doctors in their medical diagnosis. However, there is still enormous data to process which takes up time and computer energy. In this scheme, we take a step further and deploy this Neural Network (NN) on a Xilinx Cloud FPGA platform which as devices are proven to be fast and power efficient. The aim is to have a medical solution on the Cloud for hospitals in order to facilitate the medical diagnosis with accuracy, speed and power efficiency. To the best of our knowledge, this application has not yet been considered for FPGAs while the accuracy and speed achieved surpasses any previous known implementation of NNs for X-Ray Covid detection. Specifically, it can classify X-Ray images at a rate of 3600 FPS with 96.2 % accuracy and a speed-up of 3.1 × vs GPU, 17.6 × vs CPU in performance and 4.6 × vs GPU, 13.1 × vs CPU in power efficiency. © 2021, Springer Nature Switzerland AG.