A COVID-19 Detection Algorithm Using Deep Features and Discrete Social Learning Particle Swarm Optimization for Edge Computing Devices

Author:

Shen Chaonan1,Zhang Kai2,Tang Jinshan3

Affiliation:

1. Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System, Wuhan, Hubei, China

2. School of Computer Science and Technology, Wuhan University of Science and Technology, Wuhan, Hubei, China

3. Department of Health Administration and Policy, George Mason University, Fairfax, VA, USA

Abstract

COVID-19 has been spread around the world and has caused a huge number of deaths. Early detection of this disease is the most efficient way to prevent its rapid spread. Due to the development of internet technology and edge intelligence, developing an early detection system for COVID-19 in the medical environment of the Internet of Things (IoT) can effectively alleviate the spread of the disease. In this paper, a detection algorithm is developed, which can detect COVID-19 effectively by utilizing the features from Chest X-ray (CXR) images. First, a pre-trained model (ResNet18) is adopted for feature extraction. Then, a discrete social learning particle swarm optimization algorithm (DSLPSO) is proposed for feature selection. By filtering redundant and irrelevant features, the dimensionality of the feature vector is reduced. Finally, the images are classified by a Support Vector Machine (SVM) for COVID-19 detection. Experimental results show that the proposed algorithm can achieve competitive performance with fewer features, which is suitable for edge computing devices with lower computation power.

Publisher

Association for Computing Machinery (ACM)

Subject

Computer Networks and Communications

Reference45 articles.

1. The continuing 2019-nCoV epidemic threat of novel coronaviruses to global health — The latest 2019 novel coronavirus outbreak in Wuhan, China

2. Clinical Characteristics of 138 Hospitalized Patients With 2019 Novel Coronavirus–Infected Pneumonia in Wuhan, China

3. Sample-efficient deep learning for COVID-19 diagnosis based on CT scans;He Xuehai;medRxiv,2020

4. COVID-19 detection using deep learning models to exploit social mimic optimization and structured chest X-ray images using fuzzy color and stacking approaches;Toğaçar Mesut;Comput. Biol. Med.,2020

5. World Health Organization. 2020. Laboratory testing for 2019 novel coronavirus (2019-nCoV) in suspected human cases. Retrieved from https://www.who.int/publications/i/item/10665-331501.

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