# Deep Learning for Screening COVID-19 using Chest X-Ray Images

arXiv: https://arxiv.org/abs/2004.10507 · HTML: https://mlsanity.com/paper/2004.10507/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Apr 2020.

## Built on by (most cited first, 16 of 16)

- [COVID-19 Modeling: A Review](https://mlsanity.com/paper/2104.12556/) (2021)
- [COVID-19 Detection in Chest X-ray Images Using Swin-Transformer and Transformer in Transformer](https://mlsanity.com/paper/2110.08427/) (2021)
- [Using Causal Analysis for Conceptual Deep Learning Explanation](https://mlsanity.com/paper/2107.06098/) (2021)
- [MultiNet with Transformers: A Model for Cancer Diagnosis Using Images](https://mlsanity.com/paper/2301.09007/) (2023)
- [Dealing with Distribution Mismatch in Semi-supervised Deep Learning for Covid-19 Detection Using Chest X-ray Images: A Novel Approach Using Feature Densities](https://mlsanity.com/paper/2109.00889/) (2021)
- [Deep Learning Models May Spuriously Classify Covid-19 from X-ray Images Based on Confounders](https://mlsanity.com/paper/2102.04300/) (2021)
- [An Automated Approach for Timely Diagnosis and Prognosis of Coronavirus Disease](https://mlsanity.com/paper/2104.14116/) (2021)
- [A Survey of Machine Learning Techniques for Detecting and Diagnosing COVID-19 from Imaging](https://mlsanity.com/paper/2108.04344/) (2021)
- [CXR-Net: An Encoder-Decoder-Encoder Multitask Deep Neural Network for Explainable and Accurate Diagnosis of COVID-19 pneumonia with Chest X-ray Images](https://mlsanity.com/paper/2110.10813/) (2021)
- [Collective Intelligent Strategy for Improved Segmentation of COVID-19 from CT](https://mlsanity.com/paper/2212.12264/) (2022)
- [A Data Augmented Approach to Transfer Learning for Covid-19 Detection](https://mlsanity.com/paper/2108.02870/) (2021)
- [Comparative Analysis of Deep Learning Algorithms for Classification of COVID-19 X-Ray Images](https://mlsanity.com/paper/2110.09294/) (2021)
- [Advancing Diagnostic Precision: Leveraging Machine Learning Techniques for Accurate Detection of Covid-19, Pneumonia, and Tuberculosis in Chest X-Ray Images](https://mlsanity.com/paper/2310.06080/) (2023)
- [Robust and Interpretable COVID-19 Diagnosis on Chest X-ray Images using Adversarial Training](https://mlsanity.com/paper/2311.14227/) (2023)
- [From Lab to Pocket: A Novel Continual Learning-based Mobile Application for Screening COVID-19](https://mlsanity.com/paper/2410.12589/) (2024)
- [Comparison of Neural Models for X-ray Image Classification in COVID-19 Detection](https://mlsanity.com/paper/2501.04196/) (2025)

## Abstract (arXiv)

With the ever increasing demand for screening millions of prospective "novel coronavirus" or COVID-19 cases, and due to the emergence of high false negatives in the commonly used PCR tests, the necessity for probing an alternative simple screening mechanism of COVID-19 using radiological images (like chest X-Rays) assumes importance. In this scenario, machine learning (ML) and deep learning (DL) offer fast, automated, effective strategies to detect abnormalities and extract key features of the altered lung parenchyma, which may be related to specific signatures of the COVID-19 virus. However, the available COVID-19 datasets are inadequate to train deep neural networks. Therefore, we propose a new concept called domain extension transfer learning (DETL). We employ DETL, with pre-trained deep convolutional neural network, on a related large chest X-Ray dataset that is tuned for classifying between four classes textitviz. normal, pneumonia, otherdisease, and Covid-19. A 5-fold cross validation is performed to estimate the feasibility of using chest X-Rays to diagnose COVID-19. The initial results show promise, with the possibility of replication on bigger and more diverse data sets. The overall accuracy was measured as 90.13% ± 0.14. In order to get an idea about the COVID-19 detection transparency, we employed the concept of Gradient Class Activation Map (Grad-CAM) for detecting the regions where the model paid more attention during the classification. This was found to strongly correlate with clinical findings, as validated by experts.