# Detection of COVID19 in Chest X-Ray Images Using Transfer Learning

arXiv: https://arxiv.org/abs/2304.04161 · HTML: https://mlsanity.com/paper/2304.04161/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 29 references that are in the map.
- First posted on arXiv 9 Apr 2023.

## Builds on (most cited first, 10 of 10)

- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [ImageNet Large Scale Visual Recognition Challenge](https://mlsanity.com/paper/1409.0575/) (2014)
- [How transferable are features in deep neural networks?](https://mlsanity.com/paper/1411.1792/) (2014)
- [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)
- [Convolutional Neural Networks for Medical Image Analysis: Full Training or Fine Tuning?](https://mlsanity.com/paper/1706.00712/) (2017)
- [COVID-19 Image Data Collection](https://mlsanity.com/paper/2003.11597/) (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)
- [Viral Pneumonia Screening on Chest X-ray Images Using Confidence-Aware Anomaly Detection](https://mlsanity.com/paper/2003.12338/) (2020)
- [COVID-ResNet: A Deep Learning Framework for Screening of COVID19 from Radiographs](https://mlsanity.com/paper/2003.14395/) (2020)

## Abstract (arXiv)

COVID19 is a highly contagious disease infected millions of people worldwide. With limited testing components, screening tools such as chest radiography can assist the clinicians in the diagnosis and assessing the progress of disease. The performance of deep learning-based systems for diagnosis of COVID-19 disease in radiograph images has been encouraging. This paper investigates the concept of transfer learning using two of the most well-known VGGNet architectures, namely VGG-16 and VGG-19. The classifier block and hyperparameters are fine-tuned to adopt the models for automatic detection of Covid-19 in chest x-ray images. We generated two different datasets to evaluate the performance of the proposed system for the identification of positive Covid-19 instances in a multiclass and binary classification problems. The experimental outcome demonstrates the usefulness of transfer learning for small-sized datasets particularly in the field of medical imaging, not only to prevent over-fitting and convergence problems but also to attain optimal classification performance as well.