A Deep Neural Network for the Detection of COVID-19 from Chest X-ray Images

Abstract

The most successful machine learning technology considered for analyzing a significant amount of chest X-ray images is Deep Learning and it has the potential to cause significant influence on Covid-19 screening. In this paper, we analyze four distinct Convolutional Neural Network (CNN) state-of-the-art architectures that are Baseline Model, Vanilla CNN, VGG-16 and Siamese Model on the basis of test accuracy. The effectiveness of the models under consideration is assessed using the Chest Radiograph dataset, which is publicly available for research. In order to discover COVID-19, we used well-known deep learning algorithms for data rarity. These include employing Siamese networks using transfer learning and a few-shot learning approach. Our experiments show that using few-shot learning methodologies, we can create a COVID-19 identification model that is both efficient and effective even with limited data. With this strategy, we were able to achieve 95% accuracy, compared to 86% with Baseline model.