Deep Fractional Max Pooling Neural Network for COVID-19 Recognition.
Front Public Health
; 9: 726144, 2021.
Article
in English
| MEDLINE | ID: covidwho-1376723
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).Keywords
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Main subject:
Pneumonia
/
COVID-19
Type of study:
Prognostic study
Limits:
Humans
Language:
English
Journal:
Front Public Health
Year:
2021
Document Type:
Article
Affiliation country:
Fpubh.2021.726144
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