A Deep-Learning Framework for Improving COVID-19 CT Image Quality and Diagnostic Accuracy

TL;DR

This paper introduces a deep-learning framework that enhances COVID-19 CT images and improves diagnostic accuracy, significantly reducing testing time and aiding medical professionals.

Contribution

The study develops a novel DL-based framework, DL-FACT, combining image enhancement and classification to improve COVID-19 testing speed and accuracy.

Findings

Testing time reduced from days to minutes

COVID-19 detection accuracy up to 91%

Effective across multiple CT image sources

Abstract

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.