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Classification of COVID-19 Patients with their Severity Level from Chest CT Scans using Transfer Learning
Result and Conclusion: In our study, we have combined well-known strategies for pre-processing, feature extraction, and classification which brings us to a remarkable success rate of disease and its severity recognition with an accuracy of 96. 05% (97. 7% for Non-Severe COVID-19 images and 93% for Severe COVID-19 images).
COVID-19 Severity Classification on Chest X-ray Images
Pre-trained Resnet50, VGG16 model and SVM classifier are then used for feature extraction and classification.
Classification of COVID-19 on chest X-Ray images using Deep Learning model with Histogram Equalization and Lungs Segmentation
Result and Conclusion: Our novel approach combining well-known pre-processing techniques, feature extraction methods, and dataset balancing method, lead us to an outstanding rate of recognition of 98% for COVID-19 images over a dataset of 2470 X-ray images.
Comparison of Traditional and Hybrid Time Series Models for Forecasting COVID-19 Cases
Time series forecasting methods play critical role in estimating the spread of an epidemic.
