# A two-step explainable approach for COVID-19 computer-aided diagnosis from chest x-ray images

arXiv: https://arxiv.org/abs/2101.10223 · HTML: https://mlsanity.com/paper/2101.10223/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 24 references that are in the map.
- First posted on arXiv 25 Jan 2021.

## Builds on (most cited first, 11 of 11)

- [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)
- [CheXpert: A Large Chest Radiograph Dataset with Uncertainty Labels and Expert Comparison](https://mlsanity.com/paper/1901.07031/) (2019)
- [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)
- [COVID-Net: A Tailored Deep Convolutional Neural Network Design for Detection of COVID-19 Cases from Chest X-Ray Images](https://mlsanity.com/paper/2003.09871/) (2020)
- [Covid-19: Automatic detection from X-Ray images utilizing Transfer Learning with Convolutional Neural Networks](https://mlsanity.com/paper/2003.11617/) (2020)
- [Automatic Detection of Coronavirus Disease (COVID-19) Using X-ray Images and Deep Convolutional Neural Networks](https://mlsanity.com/paper/2003.10849/) (2020)
- [Deep Learning COVID-19 Features on CXR using Limited Training Data Sets](https://mlsanity.com/paper/2004.05758/) (2020)
- [Unveiling COVID-19 from Chest X-ray with deep learning: a hurdles race with small data](https://mlsanity.com/paper/2004.05405/) (2020)

## Abstract (arXiv)

Early screening of patients is a critical issue in order to assess immediate and fast responses against the spread of COVID-19. The use of nasopharyngeal swabs has been considered the most viable approach; however, the result is not immediate or, in the case of fast exams, sufficiently accurate. Using Chest X-Ray (CXR) imaging for early screening potentially provides faster and more accurate response; however, diagnosing COVID from CXRs is hard and we should rely on deep learning support, whose decision process is, on the other hand, "black-boxed" and, for such reason, untrustworthy. We propose an explainable two-step diagnostic approach, where we first detect known pathologies (anomalies) in the lungs, on top of which we diagnose the illness. Our approach achieves promising performance in COVID detection, compatible with expert human radiologists. All of our experiments have been carried out bearing in mind that, especially for clinical applications, explainability plays a major role for building trust in machine learning algorithms.