Transfer Learning Approach to COVID-19 Prediction from Chest X-Ray Images
Küçük Resim Yok
Tarih
2021
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Institute of Electrical and Electronics Engineers Inc.
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
All countries and societies have been severely affected by the COVID-19 pandemic in many several different ways especially in sectors like healthcare, education, tourism, and so on. During this period, researchers all over the world have been conducting studies, investigating and developing techniques to solve the problems caused by 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 COVID-19, Viral Pneumonia, or no infection. Initially, we utilized transfer learning to fine tune a number of pre-trained DenseNet, Inception-v3, Inception-ResNet-v2, 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 99%. © 2021 IEEE.
Açıklama
IEEE SMC Society;IEEE Turkey Section
2021 Innovations in Intelligent Systems and Applications Conference, ASYU 2021 -- 6 October 2021 through 8 October 2021 -- -- 174400
2021 Innovations in Intelligent Systems and Applications Conference, ASYU 2021 -- 6 October 2021 through 8 October 2021 -- -- 174400
Anahtar Kelimeler
Chest X-Ray, Covid-19, Deep Learning, Ensemble Learning, Transfer Learning, Viral Pneumonia
Kaynak
Proceedings - 2021 Innovations in Intelligent Systems and Applications Conference, ASYU 2021
WoS Q Değeri
Scopus Q Değeri
N/A
