Integration of CNN, CBMIR, and Visualization Techniques for Diagnosis and Quantification of Covid-19 Disease

Integration of CNN, CBMIR, and Visualization Techniques for Diagnosis and Quantification of Covid-19 Disease

Diagnosis techniques based on medical image modalities have higher sensitivities compared to conventional RT-PCT tests. We propose two methods for diagnosing COVID-19 disease using X-ray images and differentiating it from viral pneumonia. The diagnosis section is based on deep neural networks, and t...

Full description

Bibliographic Details
Main Authors: Alizadeh, M. (Author), Chen, Y.-W (Author), Foruzan, A.H (Author), Mohagheghi, S. (Author), Safavi, S.M (Author)
Format: Article
Language:English
Published: Institute of Electrical and Electronics Engineers Inc. 2021
Subjects:
algorithm
Algorithms
Article
computer assisted tomography
Computerized tomography
Content-based medical image retrieval
convolutional neural network
convolutional neural networks
coronavirus disease 2019
COVID-19
data analysis software
deep learning
deep neural network
Deep neural networks
diagnosis
Diagnosis
Diagnosis techniques
dimensional measurement accuracy
feature extraction
ground glass opacity
Hashing functions
human
Humans
Image enhancement
Image modality
image processing
image retrieval
Image retrieval
Image retrieval algorithms
image segmentation
isolation and purification
learning algorithm
lung
Lung
lung image processing
lung volume
Maximum intensity projection
measurement accuracy
measurement precision
Medical imaging
Neural Networks, Computer
pathology
procedures
Radiography, Thoracic
Retrieval methods
SARS-CoV-2
sensitivity and specificity
thorax radiography
Transfer learning
transfer of learning
validation process
virology
Visualization technique
X-ray image
Online Access:View Fulltext in Publisher

Internet

View Fulltext in Publisher

Similar Items