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
• ISSN: 0941-0643; 1433-3058
• DOI: 10.1007/s00521-020-05410-8

The new Coronavirus disease 2019 (COVID-19) is rapidly affecting the world population with statistics quickly falling out of date.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 paper provides a promising solution by proposing a COVID-19 detection system based on deep learning. The proposed deep learning modalities are based on convolutional neural network (CNN) and convolutional long short-term memory (ConvLSTM). Two different datasets are adopted for the simulation of the proposed modalities. The first dataset includes a set of CT images, while the second dataset includes a set of X-ray images. Both of these datasets consist of two categories: COVID-19 and normal. In addition, COVID-19 and pneumonia image categories are classified in order to validate the proposed modalities. The proposed deep learning modalities are tested on both X-ray and CT images as well as a combined dataset that includes both types of images. They achieved an accuracy of 100% and an F1 score of 100% in some cases. The simulation results reveal that the proposed deep learning modalities can be considered and adopted for quick COVID-19 screening.