# A Review on Deep Learning Techniques for the Diagnosis of Novel Coronavirus (COVID-19)

arXiv: https://arxiv.org/abs/2008.04815 · HTML: https://mlsanity.com/paper/2008.04815/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Aug 2020.

## Built on by (most cited first, 13 of 13)

- [COVID-Net CT-2: Enhanced Deep Neural Networks for Detection of COVID-19 from Chest CT Images Through Bigger, More Diverse Learning](https://mlsanity.com/paper/2101.07433/) (2021)
- [A Survey of Deep Learning Techniques for the Analysis of COVID-19 and their usability for Detecting Omicron](https://mlsanity.com/paper/2202.06372/) (2022)
- [Automatic Diagnosis of COVID-19 from CT Images using CycleGAN and Transfer Learning](https://mlsanity.com/paper/2104.11949/) (2021)
- [COVID-19 Modeling: A Review](https://mlsanity.com/paper/2104.12556/) (2021)
- [Combining a Convolutional Neural Network with Autoencoders to Predict the Survival Chance of COVID-19 Patients](https://mlsanity.com/paper/2104.08954/) (2021)
- [Smart Healthcare in the Age of AI: Recent Advances, Challenges, and Future Prospects](https://mlsanity.com/paper/2107.03924/) (2021)
- [XctDiff: Reconstruction of CT Images with Consistent Anatomical Structures from a Single Radiographic Projection Image](https://mlsanity.com/paper/2406.04679/) (2024)
- [Deep Learning Based Automated COVID-19 Classification from Computed Tomography Images](https://mlsanity.com/paper/2111.11191/) (2021)
- [Unlearning Spurious Correlations in Chest X-ray Classification](https://mlsanity.com/paper/2308.01119/) (2023)
- [Deep Learning with robustness to missing data: A novel approach to the detection of COVID-19](https://mlsanity.com/paper/2103.13833/) (2021)
- [Long-tailed Medical Diagnosis with Relation-aware Representation Learning and Iterative Classifier Calibration](https://mlsanity.com/paper/2502.03238/) (2025)
- [COVID-19 Detection in Computed Tomography Images with 2D and 3D Approaches](https://mlsanity.com/paper/2105.08506/) (2021)
- [COVID-19 Pneumonia Diagnosis Using Medical Images: Deep Learning-Based Transfer Learning Approach](https://mlsanity.com/paper/2503.12642/) (2025)

## Abstract (arXiv)

Novel coronavirus (COVID-19) outbreak, has raised a calamitous situation all over the world and has become one of the most acute and severe ailments in the past hundred years. The prevalence rate of COVID-19 is rapidly rising every day throughout the globe. Although no vaccines for this pandemic have been discovered yet, deep learning techniques proved themselves to be a powerful tool in the arsenal used by clinicians for the automatic diagnosis of COVID-19. This paper aims to overview the recently developed systems based on deep learning techniques using different medical imaging modalities like Computer Tomography (CT) and X-ray. This review specifically discusses the systems developed for COVID-19 diagnosis using deep learning techniques and provides insights on well-known data sets used to train these networks. It also highlights the data partitioning techniques and various performance measures developed by researchers in this field. A taxonomy is drawn to categorize the recent works for proper insight. Finally, we conclude by addressing the challenges associated with the use of deep learning methods for COVID-19 detection and probable future trends in this research area. This paper is intended to provide experts (medical or otherwise) and technicians with new insights into the ways deep learning techniques are used in this regard and how they potentially further works in combatting the outbreak of COVID-19.