RETRACTED: Deep Fractional Max Pooling Neural Network for COVID-19 Recognition - Wang, Shui-Hua; Satapathy, Suresh Chandra; Anderson, Donovan; Chen, Shi-Xin; Zhang, Yu-Dong (Frontiers in Public Health, 2021)
(Aim) 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 proposed 12-layer DFMPNN replaces max pooling and average pooling in ordinary neural networks with the help of a novel pooling method called fractional max-pooling (FMP). In addition, multiple-way data augmentation is employed to reduce overfitting. Model averaging is used to reduce randomness. (Results) We run our algorithm on a four-category dataset that contains COVID-19, community-acquired pneumonia, secondary pulmonary tuberculosis, and healthy control. The ten runs on the test set show that the micro-averaged F1 score of our DFMPNN is 95.88%. (Discussions) This proposed DFMPNN is superior to 10 state-of-the-art models. Besides, FMP outperforms traditional max pooling, average pooling, and L2-norm pooling.