@Article{cmc.2021.018514,
AUTHOR = {Saddam Bekhet, Monagi H. Alkinani, Reinel Tabares-Soto, M. Hassaballah},
TITLE = {An Efficient Method for Covid-19 Detection Using Light Weight Convolutional Neural Network},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {69},
YEAR = {2021},
NUMBER = {2},
PAGES = {2475--2491},
URL = {http://www.techscience.com/cmc/v69n2/43891},
ISSN = {1546-2226},
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.},
DOI = {10.32604/cmc.2021.018514}
}