Cargando…
DDCNNC: Dilated and depthwise separable convolutional neural Network for diagnosis COVID-19 via chest X-ray images
PURPOSE: As of December 21, 2020, a total of 77,670,400 cases of coronavirus disease 2019 (COVID-19) have been confirmed worldwide, 53,825,243 cases have been cured and 1,693,253 cases have died. Among the diagnostic methods of COVID-19, chest X-ray images have the advantages of fast imaging, low co...
Autores principales: | Li, Xiang, Zhai, Mengyao, Sun, Junding |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
The Authors. Publishing Services by Elsevier B.V. on behalf of KeAi Communications Co. Ltd.
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8056945/ http://dx.doi.org/10.1016/j.ijcce.2021.04.001 |
Ejemplares similares
-
WMR-DepthwiseNet: A Wavelet Multi-Resolution Depthwise Separable Convolutional Neural Network for COVID-19 Diagnosis
por: Monday, Happy Nkanta, et al.
Publicado: (2022) -
FPGA Implementation for Odor Identification with Depthwise Separable Convolutional Neural Network
por: Mo, Zhuofeng, et al.
Publicado: (2021) -
Underwater Acoustic Target Recognition Based on Depthwise Separable Convolution Neural Networks
por: Hu, Gang, et al.
Publicado: (2021) -
A Lightweight Dangerous Liquid Detection Method Based on Depthwise Separable Convolution for X-Ray Security Inspection
por: Liu, Dongming, et al.
Publicado: (2022) -
LdsConv: Learned Depthwise Separable Convolutions by Group Pruning
por: Lin, Wenxiang, et al.
Publicado: (2020)