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

Image and Video Processing 2021-12-20 v1 Computer Vision and Pattern Recognition
Authors: Garvit Goel , Jingyuan Qi , Wu-chun Feng , Guohua Cao
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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.

Keywords

covid-19 detection and diagnosis deep learning computed tomography reconstruction

Cite

@article{arxiv.2112.09216,
  title  = {A Deep-Learning Framework for Improving COVID-19 CT Image Quality and Diagnostic Accuracy},
  author = {Garvit Goel and Jingyuan Qi and Wu-chun Feng and Guohua Cao},
  journal= {arXiv preprint arXiv:2112.09216},
  year   = {2021}
}

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10 pages

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R2 v1 2026-06-24T08:21:13.091Z