A Deep-Learning Framework for Improving COVID-19 CT Image Quality and Diagnostic Accuracy
This addresses the need for faster and more accurate COVID-19 diagnosis for medical professionals, though it appears incremental as it adapts existing networks.
The authors tackled the problem of slow and inaccurate COVID-19 testing using CT images by developing a deep-learning framework that reduces turnaround time from days to minutes and improves testing accuracy up to 91%.
We present a deep-learning based computing framework for fast-and-accurate CT (DL-FACT) testing of COVID-19. Our CT-based DL framework was developed to improve the testing speed and accuracy of COVID-19 (plus its variants) via a DL-based approach for CT image enhancement and classification. The image enhancement network is adapted from DDnet, short for DenseNet and Deconvolution based network. To demonstrate its speed and accuracy, we evaluated DL-FACT across several sources of COVID-19 CT images. Our results show that DL-FACT can significantly shorten the turnaround time from days to minutes and improve the COVID-19 testing accuracy up to 91%. DL-FACT could be used as a software tool for medical professionals in diagnosing and monitoring COVID-19.