Deep Fractional Max Pooling Neural Network for COVID-19 Recognition

• 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
• ISSN: 2296-2565

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).