AUTHOR=Wang Shui-Hua , Satapathy Suresh Chandra , Anderson Donovan , Chen Shi-Xin , Zhang Yu-Dong 

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

JOURNAL=Frontiers in Public Health

VOLUME=9

YEAR=2021

URL=https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2021.726144

DOI=10.3389/fpubh.2021.726144

ISSN=2296-2565

ABSTRACT=<p><bold>Aim:</bold> 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.</p><p><bold>Methods:</bold> 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.</p><p><bold>Results:</bold> 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%.</p><p><bold>Discussions:</bold> This proposed DFMPNN is superior to 10 state-of-the-art models. Besides, FMP outperforms traditional MP, AP, and L2-norm pooling (L2P).</p>