Deep Fractional Max Pooling Neural Network for COVID-19 Recognition

Deep Fractional Max Pooling Neural Network for COVID-19 Recognition

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 ave...

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Bibliographic Details
Main Authors: Shui-Hua Wang, Suresh Chandra Satapathy, Donovan Anderson, Shi-Xin Chen, Yu-Dong Zhang
Format: Article
Language:English
Published: Frontiers Media S.A. 2021-08-01
Series:Frontiers in Public Health
Subjects:
convolutional neural network
fractional max pooling
data augmentation
COVID-19
average pooling
model averaging
Online Access:https://www.frontiersin.org/articles/10.3389/fpubh.2021.726144/full

Internet

https://www.frontiersin.org/articles/10.3389/fpubh.2021.726144/full

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