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20240918231927.0 |
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231226s2023 xx |||||o 00| ||eng c |
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|a 10.1016/j.eswa.2023.120023
|2 doi
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|a pubmed24n1538.xml
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|a (DE-627)NLM355678284
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|a (NLM)37063778
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|a DE-627
|b ger
|c DE-627
|e rakwb
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|a eng
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| 100 |
1 |
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|a Hadi, Muhammad Usman
|e verfasserin
|4 aut
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| 245 |
1 |
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|a A lightweight CORONA-NET for COVID-19 detection in X-ray images
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|c 2023
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| 336 |
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|a Text
|b txt
|2 rdacontent
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|a ƒaComputermedien
|b c
|2 rdamedia
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|a ƒa Online-Ressource
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|2 rdacarrier
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|a Date Revised 18.09.2024
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|a published: Print-Electronic
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|a Citation Status PubMed-not-MEDLINE
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|a © 2023 The Author(s).
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|a Since December 2019, COVID-19 has posed the most serious threat to living beings. With the advancement of vaccination programs around the globe, the need to quickly diagnose COVID-19 in general with little logistics is fore important. As a consequence, the fastest diagnostic option to stop COVID-19 from spreading, especially among senior patients, should be the development of an automated detection system. This study aims to provide a lightweight deep learning method that incorporates a convolutional neural network (CNN), discrete wavelet transform (DWT), and a long short-term memory (LSTM), called CORONA-NET for diagnosing COVID-19 from chest X-ray images. In this system, deep feature extraction is performed by CNN, the feature vector is reduced yet strengthened by DWT, and the extracted feature is detected by LSTM for prediction. The dataset included 3000 X-rays, 1000 of which were COVID-19 obtained locally. Within minutes of the test, the proposed test platform's prototype can accurately detect COVID-19 patients. The proposed method achieves state-of-the-art performance in comparison with the existing deep learning methods. We hope that the suggested method will hasten clinical diagnosis and may be used for patients in remote areas where clinical labs are not easily accessible due to a lack of resources, location, or other factors
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| 650 |
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|a Journal Article
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| 650 |
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4 |
|a CORONA-NET
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| 650 |
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4 |
|a COVID-19
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| 650 |
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4 |
|a Convolutional neural network
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| 650 |
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4 |
|a Deep learning
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| 650 |
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4 |
|a Discrete wavelet transform
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| 650 |
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|a Long short-term memory
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| 700 |
1 |
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|a Qureshi, Rizwan
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Ahmed, Ayesha
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Iftikhar, Nadeem
|e verfasserin
|4 aut
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| 773 |
0 |
8 |
|i Enthalten in
|t Expert systems with applications
|d 1999
|g 225(2023) vom: 01. Sept., Seite 120023
|w (DE-627)NLM098196782
|x 0957-4174
|7 nnns
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| 773 |
1 |
8 |
|g volume:225
|g year:2023
|g day:01
|g month:09
|g pages:120023
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| 856 |
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|u http://dx.doi.org/10.1016/j.eswa.2023.120023
|3 Volltext
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