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RETRACTED: Deep Fractional Max Pooling Neural Network for COVID-19 Recognition

Frontiers in public health, 2021-08, Vol.9 [Peer Reviewed Journal]

EISSN: 2296-2565 ;DOI: 10.3389/fpubh.2021.726144

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  • Title:
    RETRACTED: Deep Fractional Max Pooling Neural Network for COVID-19 Recognition
  • Author: Shui-Hua Wang ; Suresh Chandra Satapathy ; Donovan Anderson ; Shi-Xin Chen ; Yu-Dong Zhang
  • Subjects: average pooling ; convolutional neural network ; COVID-19 ; data augmentation ; fractional max pooling ; model averaging
  • Is Part Of: Frontiers in public health, 2021-08, Vol.9
  • Description: Aim: Coronavirus disease 2019 (COVID-19) is a form of disease triggered by a new strain of coronavirus. This paper proposes a novel model termed “deep fractional max pooling neural network (DFMPNN)” to diagnose COVID-19 more efficiently.Methods: This 12-layer DFMPNN replaces max pooling (MP) and average pooling (AP) in ordinary neural networks with the help of a novel pooling method called “fractional max-pooling” (FMP). In addition, multiple-way data augmentation (DA) is employed to reduce overfitting. Model averaging (MA) is used to reduce randomness.Results: We ran our algorithm on a four-category dataset that contained COVID-19, community-acquired pneumonia, secondary pulmonary tuberculosis (SPT), and healthy control (HC). The 10 runs on the test set show that the micro-averaged F1 (MAF) score of our DFMPNN is 95.88%.Discussions: This proposed DFMPNN is superior to 10 state-of-the-art models. Besides, FMP outperforms traditional MP, AP, and L2-norm pooling (L2P).
  • Publisher: Frontiers Media S.A
  • Language: English
  • Identifier: EISSN: 2296-2565
    DOI: 10.3389/fpubh.2021.726144
  • Source: Directory of Open Access Scholarly Resources (ROAD)
    PubMed Central (Open access)
    Geneva Foundation Free Medical Journals at publisher websites
    DOAJ Directory of Open Access Journals
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