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Frontiers in Signal Processing
FSP > Volume 5, Number 1, January 2021

Design of an Online Detection System for COVID-19 based on Attention Mechanism

Download PDF  (1062.2 KB)PP. 1-8,  Pub. Date:January 13, 2021
DOI: 10.22606/fsp.2021.51001

Author(s)
Fan Liu, Wei Xiang, Li Wang, Jintao Zhang, Jiacheng Li
Affiliation(s)
College of Electrical & Information Engineering, Southwest Minzu University, Chendu, 610041, China
College of Electrical & Information Engineering, Southwest Minzu University, Chendu, 610041, China
College of Electrical & Information Engineering, Southwest Minzu University, Chendu, 610041, China
College of Electrical & Information Engineering, Southwest Minzu University, Chendu, 610041, China
College of Electrical & Information Engineering, Southwest Minzu University, Chendu, 610041, China
Abstract
Corona Virus Disease 2019 (COVID-19) is an acute respiratory infection caused by the 2019 novel coronavirus infection that was discovered in some hospitals in Wuhan, China in December 2019. From a global perspective, COVID-19 epidemic is still in a pandemic period. Every country is taking timely preventive measures. Therefore, it is necessary to use Artificial Intelligence (AI) identification to assist radiologists in diagnosing COVID-19. This paper proposes a network model of dynamic self-attention machine for pneumonia images. The model adopts DPN92 and GCNet. It implements CR images and CT image classification of COVID-19. A COVID-19 detection system based on dynamic auto-attention machine is designed. The lung CT or CR images that meet the design requirements will be uploaded through the browser, and the background server will analyze and process the lung images, and finally output the detection category of the lung images.
Keywords
B/S model, SRGAN, GCNet, DPN92, COVID-19, CR, CT, image classification.
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