Deep Fractional Max Pooling Neural Network for COVID-19 Recognition

Shui Hua Wang, Suresh Chandra Satapathy, Donovan Anderson, Shi Xin Chen*, Yu Dong Zhang*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

15 Citations (Scopus)

Abstract

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).

Original languageEnglish
Article number726144
JournalFrontiers in Public Health
Volume9
DOIs
Publication statusPublished - 10 Aug 2021
Externally publishedYes

Keywords

  • COVID-19
  • average pooling
  • convolutional neural network
  • data augmentation
  • fractional max pooling
  • model averaging

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