# A Deep Convolutional Neural Network for COVID-19 Detection Using Chest X-Rays

arXiv: https://arxiv.org/abs/2005.01578 · HTML: https://mlsanity.com/paper/2005.01578/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Apr 2020.

## Built on by (most cited first, 11 of 11)

- [Improving deep neural network generalization and robustness to background bias via layer-wise relevance propagation optimization](https://mlsanity.com/paper/2202.00232/) (2022)
- [COVID-19 detection using chest X-rays: is lung segmentation important for generalization?](https://mlsanity.com/paper/2104.06176/) (2021)
- [Explainable Artificial Intelligence Methods in Combating Pandemics: A Systematic Review](https://mlsanity.com/paper/2112.12705/) (2021)
- [Deep Learning Models May Spuriously Classify Covid-19 from X-ray Images Based on Confounders](https://mlsanity.com/paper/2102.04300/) (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)
- [Fitness Dependent Optimizer with Neural Networks for COVID-19 patients](https://mlsanity.com/paper/2302.02986/) (2023)
- [Faster ISNet for Background Bias Mitigation on Deep Neural Networks](https://mlsanity.com/paper/2401.08409/) (2024)
- [Effective-LDAM: An Effective Loss Function To Mitigate Data Imbalance for Robust Chest X-Ray Disease Classification](https://mlsanity.com/paper/2407.04953/) (2024)
- [A Data Augmented Approach to Transfer Learning for Covid-19 Detection](https://mlsanity.com/paper/2108.02870/) (2021)
- [Scaling Model Checking for DNN Analysis via State-Space Reduction and Input Segmentation (Extended Version)](https://mlsanity.com/paper/2306.17323/) (2023)
- [Training with Explanations Alone: A New Paradigm to Prevent Shortcut Learning](https://mlsanity.com/paper/2407.09788/) (2024)

## Abstract (arXiv)

Purpose: We present image classifiers based on Dense Convolutional Networks and transfer learning to classify chest X-ray images according to three labels: COVID-19, pneumonia and normal. Methods: We fine-tuned neural networks pretrained on ImageNet and applied a twice transfer learning approach, using NIH ChestX-ray14 dataset as an intermediate step. We also suggested a novelty called output neuron keeping, which changes the twice transfer learning technique. In order to clarify the modus operandi of the models, we used Layer-wise Relevance Propagation (LRP) to generate heatmaps. Results: We were able to reach test accuracy of 100% on our test dataset. Twice transfer learning and output neuron keeping showed promising results improving performances, mainly in the beginning of the training process. Although LRP revealed that words on the X-rays can influence the networks' predictions, we discovered this had only a very small effect on accuracy. Conclusion: Although clinical studies and larger datasets are still needed to further ensure good generalization, the state-of-the-art performances we achieved show that, with the help of artificial intelligence, chest X-rays can become a cheap and accurate auxiliary method for COVID-19 diagnosis. Heatmaps generated by LRP improve the interpretability of the deep neural networks and indicate an analytical path for future research on diagnosis. Twice transfer learning with output neuron keeping improved performances.