ech T Press Science Computers, Materials & Continua DOI:10.32604/cmc.2021.018514 Article An Effcient Method for Covid-19 Detection Using Light Weight Convolutional Neural Network Saddam Bekhet 1, * , Monagi H. Alkinani 2 , Reinel Tabares-Soto 3 and M. Hassaballah 4 1 Faculty of Commerce, South Valley University, Qena, Egypt 2 Department of Computer Science and Artifcial Intelligence, College of Computer Science and Engineering, University of Jeddah, Jeddah, 21959, Saudi Arabia 3 Department of Electronics and Automation, Universidad Autónoma de Manizales, Manizales, 170001, Colombia 4 Department of Computer Science, Faculty of Computers and Information, South Valley University, Qena, Egypt * Corresponding Author: Saddam Bekhet. Email: saddam.bekhet@svu.edu.eg Received: 11 March 2021; Accepted: 22 April 2021 Abstract: The COVID-19 pandemic is a signifcant milestone in the modern history of civilization with a catastrophic effect on global wellbeing and mon- etary. The situation is very complex as the COVID-19 test kits are limited, therefore, more diagnostic methods must be developed urgently. A signif- cant initial step towards the successful diagnosis of the COVID-19 is the chest X-ray or Computed Tomography (CT), where any chest anomalies (e.g., lung infammation) can be easily identifed. Most hospitals possess X-ray or CT imaging equipments that can be used for early detection of COVID-19. Motivated by this, various artifcial intelligence (AI) techniques have been developed to identify COVID-19 positive patients using the chest X-ray or CT images. However, the advance of these AI-based systems and their highly tailored results are strongly bonded to high-end GPUs, which is not widely available in several countries. This paper introduces a technique for early COVID-19 diagnosis based on medical experience and light-weight Convolu- tional Neural Networks (CNNs), which does not require a custom hardware to run compared to currently available CNN models. The proposed deep learning model is built carefully and fne-tuned by removing all unnecessary parameters and layers to achieve the light-weight attribute that could run smoothly on a normal CPU (0.54% of AlexNet parameters). This model is highly benefcial for countries where high-end GPUs are luxuries. Experimental outcomes on some new benchmark datasets shows the robustness of the proposed technique robustness in recognizing COVID-19 with 96% accuracy. Keywords: Artifcial intelligence; COVID-19; chest CT; chest X-ray; deep learning 1 Introduction In January 2020, the World Health Organization announced a Public Health Emergency of International Concern (PHEIC) due to the world-wide spread of Coronavirus disease 2019 This work is licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.