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

作     者：Saddam Bekhet Monagi H.Alkinani Reinel Tabares-Soto M.Hassaballah 

作者机构：Faculty of CommerceSouth Valley UniversityQenaEgypt Department of Computer Science and Artificial IntelligenceCollege of Computer Science and EngineeringUniversity of JeddahJeddah21959Saudi Arabia Department of Electronics and AutomationUniversidad Autónoma de ManizalesManizales170001Colombia Department of Computer ScienceFaculty of Computers and InformationSouth Valley UniversityQenaEgypt 

出 版 物：《Computers, Materials & Continua》 (计算机、材料和连续体（英文）)

年 卷 期：2021年第69卷第11期

页      面：2475-2491页

核心收录：

学科分类：08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：Acknowledgement: Thanks for Dr. Maher Salama and the radiology team at South Valley University hospitals for providing the clinical feedback in the paper including comment on figures captions and validating the model outputs. Also  Dr. Monagi H. Alkinani extends his appreciation to the Deputyship for Research & Innovation  Ministry of Education in Saudi Arabia for supporting his research work through the Project Number MoE-IF-20-01 

主　　题：Artificial intelligence COVID-19 chest CT chest X-ray deep learning 

摘      要：The COVID-19 pandemic is a significant milestone in the modern history of civilization with a catastrophic effect on global wellbeing and *** 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 *** hospitals possess X-ray or CT imaging equipments that can be used for early detection of *** by this,various artificial intelligence(AI)techniques have been developed to identify COVID-19 positive patients using the chest X-ray or CT ***,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 *** 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 *** 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 *** outcomes on some new benchmark datasets shows the robustness of the proposed technique robustness in recognizing COVID-19 with 96%accuracy.