Automated Deep Transfer Learning-Based Approach for Detection of COVID-19 Infection in Chest X-rays

By N. Das, Navin Rathna Kumar, Manjit Kaur, Vijay Kumar, Dilbag Singh. Ingenierie et recherche biomedicale : IRBM = Biomedical engineering and research, 2020

319 citations recorded by the source corpus.

Graphical abstract

Subfields: Pulmonary and Respiratory Medicine, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/66703ce5e84b275370e1aa6ad946330c33d5a30c

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Radiology, Nuclear Medicine and Imaging

1. Hamid Reza Arabnia
2. Ceren Kaya
3. Khaled M. Rasheed
4. Ali Narin
5. Mohammadreza Iman
6. Ziynet Pamuk
7. M. Ahsan
8. Zahed Siddique

Citations in the corpus

• Automatic detection of coronavirus disease (COVID-19) using X-ray images and deep convolutional neural networks (2020, methodology): “40% accuracy over test data with the CNN model they suggested [9].”
• A Review of Deep Transfer Learning and Recent Advancements (2022, background): “However, if the source and target data are close enough, there is a high chance of success [16, 17, 18, 19].”