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
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Zusammenfassung: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 diagnosis at an early stage can aid in the timely cure of patients, lowering the mortality rate. In this reported research, three‐phase model is proposed for COVID‐19 detection. In Phase I, noise is removed from CT images using a denoise convolutional neural network (DnCNN). In the Phase II, the actual lesion region is segmented from the enhanced CT images by using deeplabv3 and ResNet‐18. In Phase III, segmented images are passed to the stack sparse autoencoder (SSAE) deep learning model having two stack auto‐encoders (SAE) with the selected hidden layers. The designed SSAE model is based on both SAE and softmax layers for COVID19 classification. The proposed method is evaluated on actual patient data of Pakistan Ordinance Factories and other public benchmark data sets with different scanners/mediums. The proposed method achieved global segmentation accuracy of 0.96 and 0.97 for classification. Denoise convolutional neural network regression model used for noise removal to enhance images quality. Model deeplabv3 is used as a backbone of the ResNet‐18 model to segment infected lungs region. Segmented images are further supplied to stack sparse autoencoder model for COVID‐19 classification.
Bibliographie:Review Editor
Alberto Diaspro
ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
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Review Editor: Alberto Diaspro
ISSN:1059-910X
1097-0029
1097-0029
DOI:10.1002/jemt.23913