Per-Covid-19: A Benchmark Database For Covid-19 Percentage Prediction From CT-scans

Abstract

Abstract Covid-19 infection recognition is very important step in the fighting against the new pandemic Covid-19. In fact, many methods have been used to recognize the Covid-19 infection including Reverse transcription polymerase chain reaction (RT-PCR), X-ray scan and CT-scan. In addition to the recognition of the Covid-19 infection, CT-scans can provide more important information about the evolution of this disease and its severity. With the extensive number of Covid-19 infections, estimating the Covid-19 percentage can help the intensive care to free up the resuscitation beds for the critical cases and follow other protocol for less severity cases. In this paper, we propose Covid-19 percentage estimation database. Moreover, we evaluate the performance of three Covolutional Neural Network (CNN) architectures which are ResneXt-50, Densenet-161 and Inception-v3. For the three CNN architectures, we use two loss functions which are MSE and Dynamic Huber. In addition, two pretrained scenarios are investigated (ImageNet pretrained models and X-ray pretrained models). The evaluated approaches achieved promising results, where Inception-v3 with using Dynamic Huber loss function and X-ray pretrained model achieved the best performance.