# Unveiling COVID-19 from Chest X-ray with deep learning: a hurdles race with small data

arXiv: https://arxiv.org/abs/2004.05405 · HTML: https://mlsanity.com/paper/2004.05405/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Apr 2020.

## Built on by (most cited first, 20 of 20)

- [EnD: Entangling and Disentangling deep representations for bias correction](https://mlsanity.com/paper/2103.02023/) (2021)
- [Deep Learning for Chest X-ray Analysis: A Survey](https://mlsanity.com/paper/2103.08700/) (2021)
- [A Systematic Collection of Medical Image Datasets for Deep Learning](https://mlsanity.com/paper/2106.12864/) (2021)
- [COVID-Net CXR-2: An Enhanced Deep Convolutional Neural Network Design for Detection of COVID-19 Cases from Chest X-ray Images](https://mlsanity.com/paper/2105.06640/) (2021)
- [Fusion of convolution neural network, support vector machine and Sobel filter for accurate detection of COVID-19 patients using X-ray images](https://mlsanity.com/paper/2102.06883/) (2021)
- [Classification of COVID-19 from CXR Images in a 15-class Scenario: an Attempt to Avoid Bias in the System](https://mlsanity.com/paper/2109.12453/) (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)
- [Improving COVID-19 CXR Detection with Synthetic Data Augmentation](https://mlsanity.com/paper/2112.07529/) (2021)
- [AI-Assisted Diagnosis for Covid-19 CXR Screening: From Data Collection to Clinical Validation](https://mlsanity.com/paper/2405.11598/) (2024)
- [A two-step explainable approach for COVID-19 computer-aided diagnosis from chest x-ray images](https://mlsanity.com/paper/2101.10223/) (2021)
- [Highly Efficient Representation and Active Learning Framework and Its Application to Imbalanced Medical Image Classification](https://mlsanity.com/paper/2103.05109/) (2021)
- [Ebola Optimization Search Algorithm (EOSA): A new metaheuristic algorithm based on the propagation model of Ebola virus disease](https://mlsanity.com/paper/2106.01416/) (2021)
- [Requirement analysis for an artificial intelligence model for the diagnosis of the COVID-19 from chest X-ray data](https://mlsanity.com/paper/2110.12464/) (2021)
- [A Deep Learning Approach for the Detection of COVID-19 from Chest X-Ray Images using Convolutional Neural Networks](https://mlsanity.com/paper/2201.09952/) (2022)
- [Machine learning approaches for COVID-19 detection from chest X-ray imaging: A Systematic Review](https://mlsanity.com/paper/2206.05615/) (2022)
- [Comparative Analysis of State-of-the-Art Deep Learning Models for Detecting COVID-19 Lung Infection from Chest X-Ray Images](https://mlsanity.com/paper/2208.01637/) (2022)
- [COVID-19 Detection from Exhaled Breath](https://mlsanity.com/paper/2305.19211/) (2023)
- [Gauge-optimal approximate learning for small data classification problems](https://mlsanity.com/paper/2310.19066/) (2023)
- [A Comprehensive Analysis of COVID-19 Detection Using Bangladeshi Data and Explainable AI](https://mlsanity.com/paper/2506.07234/) (2025)
- [A Comprehensive Comparison of Deep Learning Architectures for COVID-19 Classification on CT & X-ray Imagery](https://mlsanity.com/paper/2605.20445/) (2026)

## Abstract (arXiv)

The possibility to use widespread and simple chest X-ray (CXR) imaging for early screening of COVID-19 patients is attracting much interest from both the clinical and the AI community. In this study we provide insights and also raise warnings on what is reasonable to expect by applying deep-learning to COVID classification of CXR images. We provide a methodological guide and critical reading of an extensive set of statistical results that can be obtained using currently available datasets. In particular, we take the challenge posed by current small size COVID data and show how significant can be the bias introduced by transfer-learning using larger public non-COVID CXR datasets. We also contribute by providing results on a medium size COVID CXR dataset, just collected by one of the major emergency hospitals in Northern Italy during the peak of the COVID pandemic. These novel data allow us to contribute to validate the generalization capacity of preliminary results circulating in the scientific community. Our conclusions shed some light into the possibility to effectively discriminate COVID using CXR.