Open Access

ARTICLE

An Efficient Method for Covid-19 Detection Using Light Weight Convolutional Neural Network

Saddam Bekhet^1,*, Monagi H. Alkinani², Reinel Tabares-Soto³, M. Hassaballah⁴

1 Faculty of Commerce, South Valley University, Qena, Egypt
2 Department of Computer Science and Artificial 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:

(This article belongs to this Special Issue: Recent Advances in Deep Learning for Medical Image Analysis)

Computers, Materials & Continua 2021, 69(2), 2475-2491. https://doi.org/10.32604/cmc.2021.018514

Received 11 March 2021; Accepted 22 April 2021; Issue published 21 July 2021

Abstract

The COVID-19 pandemic is a significant milestone in the modern history of civilization with a catastrophic effect on global wellbeing and monetary. The situation is very complex as the COVID-19 test kits are limited, therefore, more diagnostic methods must be developed urgently. A significant 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 inflammation) can be easily identified. Most hospitals possess X-ray or CT imaging equipments that can be used for early detection of COVID-19. Motivated by this, various artificial 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 Convolutional 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 fine-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 beneficial 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.

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