Web‐based efficient dual attention networks to detect COVID‐19 from X‐ray images. Issue 24 (21st October 2020)

• Record Type: Journal Article
• Authors: Sarker, Md. Mostafa Kamal; Makhlouf, Yasmine; Banu, Syeda Furruka; Chambon, Sylvie; Radeva, Petia; Puig, Domenec
• Is Part Of: Electronics letters. Volume 56:Issue 24(2020)
• Page Start: 1298
• Page End: 1301
• Publication Date: 2020-10-21
• Subjects: Internet -- object detection -- X‐ray imaging -- medical image processing; web‐based efficient dual attention networks -- X‐ray images -- web‐based COVID‐19 detector -- EDANet architecture -- inverted residual structures -- dual attention mechanism -- COVIDx data set -- model complexity; Electronics -- Periodicals; 621.381
• DOI: 10.1049/el.2020.1962
• Languages: English
• ISSNs: 0013-5194

Abstract

Abstract : Rapid and accurate detection of COVID‐19 is a crucial step to control the virus. For this purpose, the authors designed a web‐based COVID‐19 detector using efficient dual attention networks, called 'EDANet'. The EDANet architecture is based on inverted residual structures to reduce the model complexity and dual attention mechanism with position and channel attention blocks to enhance the discriminant features from the different layers of the network. Although the EDANet has only 4.1 million parameters, the experimental results demonstrate that it achieves the state‐of‐the‐art results on the COVIDx data set in terms of accuracy and sensitivity of 96 and 94 % . The web application is available at the following link: https://covid19detector‐cxr.herokuapp.com/ .