# Reliable COVID-19 Detection Using Chest X-ray Images

arXiv: https://arxiv.org/abs/2101.12254 · HTML: https://mlsanity.com/paper/2101.12254/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 30 references that are in the map.
- First posted on arXiv 28 Jan 2021.

## Builds on (most cited first, 14 of 14)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [Rethinking the Inception Architecture for Computer Vision](https://mlsanity.com/paper/1512.00567/) (2015)
- [Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning](https://mlsanity.com/paper/1602.07261/) (2016)
- [ChestX-ray8: Hospital-scale Chest X-ray Database and Benchmarks on Weakly-Supervised Classification and Localization of Common Thorax Diseases](https://mlsanity.com/paper/1705.02315/) (2017)
- [Can AI help in screening Viral and COVID-19 pneumonia?](https://mlsanity.com/paper/2003.13145/) (2020)
- [COVID-19 Image Data Collection](https://mlsanity.com/paper/2003.11597/) (2020)
- [BIMCV COVID-19+: a large annotated dataset of RX and CT images from COVID-19 patients](https://mlsanity.com/paper/2006.01174/) (2020)
- [COVID-19 Infection Map Generation and Detection from Chest X-Ray Images](https://mlsanity.com/paper/2009.12698/) (2020)
- [COVID_MTNet: COVID-19 Detection with Multi-Task Deep Learning Approaches](https://mlsanity.com/paper/2004.03747/) (2020)
- [PDCOVIDNet: A Parallel-Dilated Convolutional Neural Network Architecture for Detecting COVID-19 from Chest X-Ray Images](https://mlsanity.com/paper/2007.14777/) (2020)
- [COVID-19 Classification of X-ray Images Using Deep Neural Networks](https://mlsanity.com/paper/2010.01362/) (2020)

## Built on by (most cited first, 5 of 5)

- [OSegNet: Operational Segmentation Network for COVID-19 Detection using Chest X-ray Images](https://mlsanity.com/paper/2202.10185/) (2022)
- [Open Challenges and Opportunities in Federated Foundation Models Towards Biomedical Healthcare](https://mlsanity.com/paper/2405.06784/) (2024)
- [MedSG-Bench: A Benchmark for Medical Image Sequences Grounding](https://mlsanity.com/paper/2505.11852/) (2025)
- [Vision Transformer-based Model for Severity Quantification of Lung Pneumonia Using Chest X-ray Images](https://mlsanity.com/paper/2303.11935/) (2023)
- [PaCX-MAE: Physiology-Augmented Chest X-Ray Masked Autoencoder](https://mlsanity.com/paper/2606.01537/) (2026)

## Abstract (arXiv)

Coronavirus disease 2019 (COVID-19) has emerged the need for computer-aided diagnosis with automatic, accurate, and fast algorithms. Recent studies have applied Machine Learning algorithms for COVID-19 diagnosis over chest X-ray (CXR) images. However, the data scarcity in these studies prevents a reliable evaluation with the potential of overfitting and limits the performance of deep networks. Moreover, these networks can discriminate COVID-19 pneumonia usually from healthy subjects only or occasionally, from limited pneumonia types. Thus, there is a need for a robust and accurate COVID-19 detector evaluated over a large CXR dataset. To address this need, in this study, we propose a reliable COVID-19 detection network: ReCovNet, which can discriminate COVID-19 pneumonia from 14 different thoracic diseases and healthy subjects. To accomplish this, we have compiled the largest COVID-19 CXR dataset: QaTa-COV19 with 124,616 images including 4603 COVID-19 samples. The proposed ReCovNet achieved a detection performance with 98.57% sensitivity and 99.77% specificity.