Cargando…
Deep Fractional Max Pooling Neural Network for COVID-19 Recognition
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 av...
Autores principales: | Wang, Shui-Hua, Satapathy, Suresh Chandra, Anderson, Donovan, Chen, Shi-Xin, Zhang, Yu-Dong |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8383320/ https://www.ncbi.nlm.nih.gov/pubmed/34447739 http://dx.doi.org/10.3389/fpubh.2021.726144 |
Ejemplares similares
-
A five-layer deep convolutional neural network with stochastic pooling for chest CT-based COVID-19 diagnosis
por: Zhang, Yu-Dong, et al.
Publicado: (2020) -
Improving ductal carcinoma in situ classification by convolutional neural network with exponential linear unit and rank-based weighted pooling
por: Zhang, Yu-Dong, et al.
Publicado: (2020) -
PSCNN: PatchShuffle Convolutional Neural Network for COVID-19 Explainable Diagnosis
por: Wang, Shui-Hua, et al.
Publicado: (2021) -
Early recognition of risk of critical adverse events based on deep neural decision gradient boosting
por: Chen, Yu-wen, et al.
Publicado: (2023) -
COVID-19 classification using chest X-ray images: A framework of CNN-LSTM and improved max value moth flame optimization
por: Hamza, Ameer, et al.
Publicado: (2022)