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COVID-19 Diagnosis from CT Images with Convolutional Neural Network Optimized by Marine Predator Optimization Algorithm.

In recent years, almost every country in the world has struggled against the spread of Coronavirus Disease 2019. If governments and public health systems do not take action against the spread of the disease, it will have a severe impact on human life. A noteworthy technique to stop this pandemic is...

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Published in:BioMed Research International pp. 1 - 10
Main Authors: Jia, Huaping, Zhao, Junlong, Arshaghi, Ali
Format: diagnostic images equations & formulas tables/charts Journal Article
Published: Wiley-Blackwell 10/12/2021
Online Access:View this record in EBSCOhost
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      jtl: BioMed Research International
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      dt: 10/12/2021
      pid: 480
      pub: Wiley-Blackwell
      place: Malden, Massachusetts
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        10.1155/2021/5122962
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        atl: COVID-19 Diagnosis from CT Images with Convolutional Neural Network Optimized by Marine Predator Optimization Algorithm.
      aug:
        au:
          Jia, Huaping
          Zhao, Junlong
          Arshaghi, Ali
        affil: College of Computer, Weinan Normal University, Weinan, Shaanxi, China
      sug:
        subj:
          COVID-19 Radiography
          Tomography, X-Ray Computed
          Neural Networks (Computer)
          Algorithms
          Simulations
      ab: In recent years, almost every country in the world has struggled against the spread of Coronavirus Disease 2019. If governments and public health systems do not take action against the spread of the disease, it will have a severe impact on human life. A noteworthy technique to stop this pandemic is diagnosing COVID-19 infected patients and isolating them instantly. The present study proposes a method for the diagnosis of COVID-19 from CT images. The method is a hybrid method based on convolutional neural network which is optimized by a newly introduced metaheuristic, called marine predator optimization algorithm. This optimization method is performed to improve the system accuracy. The method is then implemented on the chest CT scans with the COVID-19-related findings (MosMedData) dataset, and the results are compared with three other methods from the literature to indicate the method's performance. The final results indicate that the proposed method with 98.11% accuracy, 98.13% precision, 98.66% sensitivity, and 97.26% F 1 score has the highest performance in all indicators than the compared methods which shows its higher accuracy and reliability.
      pubtype: Academic Journal
      doctype:
        diagnostic images
        equations & formulas
        tables/charts
        Journal Article
      ougenre: Article
    language: English
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