Journal
FRONTIERS IN PUBLIC HEALTH
Volume 9, Issue -, Pages -Publisher
FRONTIERS MEDIA SA
DOI: 10.3389/fpubh.2021.726144
Keywords
convolutional neural network; fractional max pooling; data augmentation; COVID-19; average pooling; model averaging
Categories
Funding
- Sino-UK Industrial Fund, UK [RP202G0289]
- Global Challenges Research Fund (GCRF), UK [P202PF11]
- Royal Society International Exchanges Cost Share Award, UK [RP202G0230]
- Medical Research Council Confidence in Concept Award, UK [MC_PC_17171]
- Hope Foundation for Cancer Research, UK [RM60G0680]
- British Heart Foundation Accelerator Award, UK
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This paper introduces a new model called DFMPNN to diagnose COVID-19 more efficiently, achieving a micro-averaged F1 score of 95.88% through techniques such as fractional max-pooling, multiple-way data augmentation, and model averaging. Experimental results show that DFMPNN outperforms 10 state-of-the-art models in disease diagnosis.
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).
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