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Cited by 691

The DOI matches its Crossref record; 9 of the 9 references it cites were found in a registry or an index.

A combined deep CNN-LSTM network for the detection of novel coronavirus (COVID-19) using X-ray images

Open access
Islam, Md. Zabirul, Islam, Md. Milon, Asraf, Amanullah·2020·Informatics in Medicine Unlocked
DOI 10.1016/j.imu.2020.100412

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Abstract

Nowadays, automatic disease detection has become a crucial issue in medical science due to rapid population growth. An automatic disease detection framework assists doctors in the diagnosis of disease and provides exact, consistent, and fast results and reduces the death rate. Coronavirus (COVID-19) has become one of the most severe and acute diseases in recent times and has spread globally. Therefore, an automated detection system, as the fastest diagnostic option, should be implemented to impede COVID-19 from spreading. This paper aims to introduce a deep learning technique based on the combination of a convolutional neural network (CNN) and long short-term memory (LSTM) to diagnose COVID-19 automatically from X-ray images. In this system, CNN is used for deep feature extraction and LSTM is used for detection using the extracted feature. A collection of 4575 X-ray images, including 1525 images of COVID-19, were used as a dataset in this system. The experimental results show that our proposed system achieved an accuracy of 99.4%, AUC of 99.9%, specificity of 99.2%, sensitivity of 99.3%, and F1-score of 98.9%. The system achieved desired results on the currently available dataset, which can be further improved when more COVID-19 images become available. The proposed system can help doctors to diagnose and treat COVID-19 patients easily.

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Citations

Cites

Show all 9 references

Research works printed but not linked: 23

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Metadata

Authors
Islam, M. Z., Islam, M. M., & Asraf, A.
Venue
Informatics in Medicine Unlocked
Publisher
Elsevier BV
Published
2020
Volume
20
Pages
100412
ISSN
2352-9148 (ISSN-L)
DOI
10.1016/j.imu.2020.100412
OpenAlex
W3048749423
Cited by
691
References
9
FWCI
56.93
Type
journal article
Language
en
Open access
gold
Licence
Funders
Case School of Engineering, Case Western Reserve University

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CoChat. (2026). Citation check: A combined deep CNN-LSTM network for the detection of novel coronavirus (COVID-19) using X-ray images. https://app.cochat.ai/papers/10.1016/j.imu.2020.100412
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Islam, M. Z., Islam, M. M., & Asraf, A. (2020). A combined deep CNN-LSTM network for the detection of novel coronavirus (COVID-19) using X-ray images. Informatics in Medicine Unlocked. https://doi.org/10.1016/j.imu.2020.100412

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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.