Microscopic segmentation and classification of COVID‐19 infection with ensemble convolutional neural network

The detection of biological RNA from sputum has a comparatively poor positive rate in the initial/early stages of discovering COVID‐19, as per the World Health Organization. It has a different morphological structure as compared to healthy images, manifested by computer tomography (CT). COVID‐19 dia...

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Veröffentlicht in:	Microscopy research and technique Jg. 85; H. 1; S. 385 - 397
Hauptverfasser:	Amin, Javeria, Anjum, Muhammad Almas, Sharif, Muhammad, Rehman, Amjad, Saba, Tanzila, Zahra, Rida
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Hoboken, USA John Wiley & Sons, Inc 01.01.2022 Wiley Subscription Services, Inc
Schlagworte:	Artificial neural networks Classification Coders Computed tomography COVID-19 COVID-19 Testing Deep learning Deeplabv3 denoise convolutional neural network (DnCNN) healthcare Humans Image enhancement Image segmentation Machine learning Medical imaging Neural networks Neural Networks, Computer public health ResNet‐18 SARS-CoV-2 Sputum stack sparse autoencoder deep learning model (SSAE) Tomography, X-Ray Computed Deeplabv3 ResNet-18 denoise convolutional neural network (DnCNN) public health stack sparse autoencoder deep learning model (SSAE) healthcare
ISSN:	1059-910X, 1097-0029, 1097-0029
Online-Zugang:	Volltext
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