# Artificial Intelligence applied to chest X-Ray images for the automatic detection of COVID-19. A thoughtful evaluation approach

arXiv: https://arxiv.org/abs/2011.14259 · HTML: https://mlsanity.com/paper/2011.14259/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Nov 2020.

## Built on by (most cited first, 8 of 8)

- [An Accurate and Explainable Deep Learning System Improves Interobserver Agreement in the Interpretation of Chest Radiograph](https://mlsanity.com/paper/2208.03545/) (2022)
- [Explainable Artificial Intelligence Methods in Combating Pandemics: A Systematic Review](https://mlsanity.com/paper/2112.12705/) (2021)
- [Adaptive Ensemble Learning: Boosting Model Performance through Intelligent Feature Fusion in Deep Neural Networks](https://mlsanity.com/paper/2304.02653/) (2023)
- [Advancement of Deep Learning in Pneumonia and Covid-19 Classification and Localization: A Qualitative and Quantitative Analysis](https://mlsanity.com/paper/2111.08606/) (2021)
- [Pneumonia Detection in Chest X-Rays using Neural Networks](https://mlsanity.com/paper/2204.03618/) (2022)
- [Machine learning approaches for COVID-19 detection from chest X-ray imaging: A Systematic Review](https://mlsanity.com/paper/2206.05615/) (2022)
- [Designing an Improved Deep Learning-based Model for COVID-19 Recognition in Chest X-ray Images: A Knowledge Distillation Approach](https://mlsanity.com/paper/2301.02735/) (2023)
- [Cascade Learning Localises Discriminant Features in Visual Scene Classification](https://mlsanity.com/paper/2311.12704/) (2023)

## Abstract (arXiv)

Current standard protocols used in the clinic for diagnosing COVID-19 include molecular or antigen tests, generally complemented by a plain chest X-Ray. The combined analysis aims to reduce the significant number of false negatives of these tests, but also to provide complementary evidence about the presence and severity of the disease. However, the procedure is not free of errors, and the interpretation of the chest X-Ray is only restricted to radiologists due to its complexity. With the long term goal to provide new evidence for the diagnosis, this paper presents an evaluation of different methods based on a deep neural network. These are the first steps to develop an automatic COVID-19 diagnosis tool using chest X-Ray images, that would additionally differentiate between controls, pneumonia or COVID-19 groups. The paper describes the process followed to train a Convolutional Neural Network with a dataset of more than 79,500 X-Ray images compiled from different sources, including more than 8,500 COVID-19 examples. For the sake of evaluation and comparison of the models developed, three different experiments were carried out following three preprocessing schemes. The aim is to evaluate how preprocessing the data affects the results and improves its explainability. Likewise, a critical analysis is carried out about different variability issues that might compromise the system and the effects on the performance. With the employed methodology, a 91.5% classification accuracy is obtained, with a 87.4% average recall for the worst but most explainable experiment, which requires a previous automatic segmentation of the lungs region.