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CO-IRv2: Optimized InceptionResNetV2 for COVID-19 detection from chest CT images
- Source :
- PLoS ONE, PLoS ONE, Vol 16, Iss 10 (2021), PLoS ONE, Vol 16, Iss 10, p e0259179 (2021)
- Publication Year :
- 2021
- Publisher :
- Public Library of Science (PLoS), 2021.
-
Abstract
- This paper focuses on the application of deep learning (DL) in the diagnosis of coronavirus disease (COVID-19). The novelty of this work is in the introduction of optimized InceptionResNetV2 for COVID-19 (CO-IRv2) method. A part of the CO-IRv2 scheme is derived from the concepts of InceptionNet and ResNet with hyperparameter tuning, while the remaining part is a new architecture consisting of a global average pooling layer, batch normalization, dense layers, and dropout layers. The proposed CO-IRv2 is applied to a new dataset of 2481 computed tomography (CT) images formed by collecting two independent datasets. Data resizing and normalization are performed, and the evaluation is run up to 25 epochs. Various performance metrics, including precision, recall, accuracy, F1-score, area under the receiver operating characteristics (AUC) curve are used as performance metrics. The effectiveness of three optimizers known as Adam, Nadam and RMSProp are evaluated in classifying suspected COVID-19 patients and normal people. Results show that for CO-IRv2 and for CT images, the obtained accuracies of Adam, Nadam and RMSProp optimizers are 94.97%, 96.18% and 96.18%, respectively. Furthermore, it is shown here that for the case of CT images, CO-IRv2 with Nadam optimizer has better performance than existing DL algorithms in the diagnosis of COVID-19 patients. Finally, CO-IRv2 is applied to an X-ray dataset of 1662 images resulting in a classification accuracy of 99.40%.
- Subjects :
- Viral Diseases
Pulmonology
Computer science
Pooling
Diagnostic Radiology
Medical Conditions
Mathematical and Statistical Techniques
Medicine and Health Sciences
Image Processing, Computer-Assisted
Tomography
Virus Testing
Hyperparameter
Multidisciplinary
Artificial neural network
Radiology and Imaging
Pulmonary Imaging
Bone Imaging
Data Accuracy
Infectious Diseases
Medicine
Algorithms
Research Article
Normalization (statistics)
Imaging Techniques
Science
Neuroimaging
Image processing
Research and Analysis Methods
Sensitivity and Specificity
Deep Learning
Diagnostic Medicine
Humans
Receiver operating characteristic
SARS-CoV-2
business.industry
Deep learning
Biology and Life Sciences
COVID-19
Covid 19
Pattern recognition
Pneumonia
Convolution
Computed Axial Tomography
X-Ray Radiography
Radiography
ROC Curve
Neural Networks, Computer
Artificial intelligence
Tomography, X-Ray Computed
business
Mathematical Functions
Neuroscience
Subjects
Details
- ISSN :
- 19326203
- Volume :
- 16
- Database :
- OpenAIRE
- Journal :
- PLOS ONE
- Accession number :
- edsair.doi.dedup.....b40bfad0523b15317934cf8847be4345