Efficient deep learning approach for augmented detection of Coronavirus disease

• Published in: Neural computing & applications Vol. 34; no. 14; pp. 11423 - 11440
• Main Authors: Sedik, Ahmed, Hammad, Mohamed, Abd El-Samie, Fathi E., Gupta, Brij B., Abd El-Latif, Ahmed A.
• Format: Journal Article
• Language: English
• Published: London Springer London 01.07.2022; Springer Nature B.V
• Subjects: Artificial Intelligence; Artificial neural networks; Computational Biology/Bioinformatics; Computational Science and Engineering; Computed tomography; Computer Science; Coronaviruses; COVID-19; Data Mining and Knowledge Discovery; Datasets; Deep learning; Image Processing and Computer Vision; Medical imaging; Probability and Statistics in Computer Science; S.I. : Healthcare Analytics; Viral diseases; Deep learning; COVID-19; Medical images; Coronavirus; Convolutional neural networks; Analysis

The new Coronavirus disease 2019 (COVID-19) is rapidly affecting the world population with statistics quickly falling out of date. Due to the limited availability of annotated Coronavirus X-ray and CT images, the detection of COVID-19 remains the biggest challenge in diagnosing this disease. This pa...