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Neural Computing and Applications
Published in: Neural Computing and Applications 14/2022

19-01-2021 | S.I. : Healthcare Analytics

Efficient deep learning approach for augmented detection of Coronavirus disease

Authors: Ahmed Sedik, Mohamed Hammad, Fathi E. Abd El-Samie, Brij B. Gupta, Ahmed A. Abd El-Latif

Published in: Neural Computing and Applications | Issue 14/2022

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Abstract

The new Coronavirus disease 2019 (COVID-19) is rapidly affecting the world population with statistics quickly falling out of date.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.
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Metadata
Title
Efficient deep learning approach for augmented detection of Coronavirus disease
Authors
Ahmed Sedik
Mohamed Hammad
Fathi E. Abd El-Samie
Brij B. Gupta
Ahmed A. Abd El-Latif
Publication date
19-01-2021
Publisher
Springer London
Published in
Neural Computing and Applications / Issue 14/2022
Print ISSN: 0941-0643
Electronic ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-020-05410-8

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