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231226s2023 xx |||||o 00| ||eng c |
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|a 10.1080/02664763.2020.1849057
|2 doi
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|a pubmed25n1177.xml
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|a (DE-627)NLM353253308
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|a (NLM)36819076
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|a DE-627
|b ger
|c DE-627
|e rakwb
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| 041 |
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|a eng
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| 100 |
1 |
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|a Pustokhin, Denis A
|e verfasserin
|4 aut
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| 245 |
1 |
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|a An effective deep residual network based class attention layer with bidirectional LSTM for diagnosis and classification of COVID-19
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| 264 |
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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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| 337 |
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|a ƒaComputermedien
|b c
|2 rdamedia
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| 338 |
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|a ƒa Online-Ressource
|b cr
|2 rdacarrier
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|a Date Revised 24.02.2023
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|a published: Electronic-eCollection
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|a Citation Status PubMed-not-MEDLINE
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|a © 2020 Informa UK Limited, trading as Taylor & Francis Group.
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|a In recent days, COVID-19 pandemic has affected several people's lives globally and necessitates a massive number of screening tests to detect the existence of the coronavirus. At the same time, the rise of deep learning (DL) concepts helps to effectively develop a COVID-19 diagnosis model to attain maximum detection rate with minimum computation time. This paper presents a new Residual Network (ResNet) based Class Attention Layer with Bidirectional LSTM called RCAL-BiLSTM for COVID-19 Diagnosis. The proposed RCAL-BiLSTM model involves a series of processes namely bilateral filtering (BF) based preprocessing, RCAL-BiLSTM based feature extraction, and softmax (SM) based classification. Once the BF technique produces the preprocessed image, RCAL-BiLSTM based feature extraction process takes place using three modules, namely ResNet based feature extraction, CAL, and Bi-LSTM modules. Finally, the SM layer is applied to categorize the feature vectors into corresponding feature maps. The experimental validation of the presented RCAL-BiLSTM model is tested against Chest-X-Ray dataset and the results are determined under several aspects. The experimental outcome pointed out the superior nature of the RCAL-BiLSTM model by attaining maximum sensitivity of 93.28%, specificity of 94.61%, precision of 94.90%, accuracy of 94.88%, F-score of 93.10% and kappa value of 91.40%
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| 650 |
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4 |
|a Journal Article
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| 650 |
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4 |
|a COVID-19
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| 650 |
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4 |
|a Chest-X-Ray
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| 650 |
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4 |
|a deep learning
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| 650 |
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4 |
|a feature extraction
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| 650 |
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4 |
|a machine learning
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| 700 |
1 |
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|a Pustokhina, Irina V
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Dinh, Phuoc Nguyen
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Phan, Son Van
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Nguyen, Gia Nhu
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Joshi, Gyanendra Prasad
|e verfasserin
|4 aut
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| 700 |
1 |
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|a K, Shankar
|e verfasserin
|4 aut
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| 773 |
0 |
8 |
|i Enthalten in
|t Journal of applied statistics
|d 1991
|g 50(2023), 3 vom: 23., Seite 477-494
|w (DE-627)NLM098188178
|x 0266-4763
|7 nnas
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| 773 |
1 |
8 |
|g volume:50
|g year:2023
|g number:3
|g day:23
|g pages:477-494
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| 856 |
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|u http://dx.doi.org/10.1080/02664763.2020.1849057
|3 Volltext
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