Deep Transfer Learning Based Classification Model for COVID-19 Disease

By Y. Pathak, P. Shukla, A. Tiwari, S. Stalin, S. Singh. Ingenierie et recherche biomedicale : IRBM = Biomedical engineering and research, 2020

408 citations recorded by the source corpus.

Graphical abstract

Subfields: Artificial Intelligence, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/eb533b2830af187a1e0954b08ff7d249a80673a9

How researchers see this paper

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

In Artificial Intelligence

1. Yongxin Liu
2. Su Ruan
3. Shuteng Niu
4. Hua Li
5. Jian Wang
6. Romain Modzelewski
7. Houbing Song
8. Amine Amyar

In Radiology, Nuclear Medicine and Imaging

1. Demosthenes Panagiotakos
2. Fang Liu

Citations in the corpus

• A Decade Survey of Transfer Learning (2010–2020) (2020, positive): “Especially, TL methods also benefit a number of COVID-19 related problems [111]–[113], such as detection, treatment, and spread prediction.”
• Multi-task deep learning based CT imaging analysis for COVID-19 pneumonia: Classification and segmentation (2020, positive): “Artificial intelligence has seen a major and rapid growth in recent years with deep neural networks [43] as a first tool to solve different problems such as object detection [29,37]speech recognition [37], drug interaction [27] and image classification [10].”