COVID-19 Prediction from Chest X-Ray Images using Transfer Learning

Abstract

The COVID-19 pandemic has been affecting our lives in many ways, not only the healthcare systems in the countries but the whole societies worldwide. Meantime, a considerable number of studies have been conducted and lots of medical techniques have been tried to overcome the pandemic. In this work, making use of real-world images, we applied Convolutional Neural Networks to chest X-ray images to predict whether a patient has the COVID-19 virus or not. Initially, we used transfer learning to fine tune a number of pre-trained ResNet, VGG, and Xception models, which are very well-known architectures due to their success in image processing tasks. While the achieved performance with these models was encouraging, we ensembled three models to obtain more accurate and reliable results. Finally, our ensemble model outperformed all other models with an F-Score of 97%.

Keywords

• göğüs röntgeni
• covid-19
• viral pnömoni
• derin öğrenme
• transfer öğrenme
• topluluk öğrenmesi
• chest x-ray
• covid-19
• viral pneumonia
• deep learning
• transfer learning
• ensemble learning