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COVID-19 Infection Localization and Severity Grading from Chest X-ray Images
In this study, we address this urgent need by proposing a systematic and unified approach for lung segmentation and COVID-19 localization with infection quantification from CXR images.
EDITH :ECG biometrics aided by Deep learning for reliable Individual auTHentication
In this work, we present, EDITH, a deep learning-based framework for ECG biometrics authentication system.
Detection and severity classification of COVID-19 in CT images using deep learning
Furthermore, the proposed system achieved an elegant performance for COVID-19 infection segmentation with a DSC of 94. 13% and IoU of 91. 85% using the FPN model with the DenseNet201 encoder.
Exploring the Effect of Image Enhancement Techniques on COVID-19 Detection using Chest X-rays Images
The accuracy, precision, sensitivity, f1-score, and specificity in the detection of COVID-19 with gamma correction on CXR images were 96. 29%, 96. 28%, 96. 29%, 96. 28% and 96. 27% respectively.
Deep Learning for Reliable Classification of COVID-19, MERS, and SARS from Chest X-Ray Images
All networks showed high COVID-19 detection sensitivity (>96%) with the segmented lung images.
