About: COVID-19 detection using deep learning models to exploit Social Mimic Optimization and structured chest X-ray images using fuzzy color and stacking approaches

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About: COVID-19 detection using deep learning models to exploit Social Mimic Optimization and structured chest X-ray images using fuzzy color and stacking approaches Goto Sponge NotDistinct Permalink

An Entity of Type : schema:ScholarlyArticle, within Data Space : wasabi.inria.fr associated with source document(s)

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
has title	COVID-19 detection using deep learning models to exploit Social Mimic Optimization and structured chest X-ray images using fuzzy color and stacking approaches
Creator	Cömert, Zafer Ergen, Burhan Toğaçar, Mesut
Source	Elsevier; Medline; PMC
abstract	Coronavirus causes a wide variety of respiratory infections and it is an RNA-type virus that can infect both humans and animal species. It often causes pneumonia in humans. Artificial intelligence models have been helpful for successful analyses in the biomedical field. In this study, Coronavirus was detected using a deep learning model, which is a sub-branch of artificial intelligence. Our dataset consists of three classes namely: coronavirus, pneumonia, and normal X-ray imagery. In this study, the data classes were restructured using the Fuzzy Color technique as a preprocessing step and the images that were structured with the original images were stacked. In the next step, the stacked dataset was trained with deep learning models (MobileNetV2, SqueezeNet) and the feature sets obtained by the models were processed using the Social Mimic optimization method. Thereafter, efficient features were combined and classified using Support Vector Machines (SVM). The overall classification rate obtained with the proposed approach was 99.27%. With the proposed approach in this study, it is evident that the model can efficiently contribute to the detection of COVID-19 disease.
has issue date	2020-05-06(xsd:dateTime)
bibo:doi	10.1016/j.compbiomed.2020.103805
bibo:pmid	32568679
has license	no-cc
sha1sum (hex)	601093cf490a2f8e5397feae0e436a0123f13f63
schema:url	https://doi.org/10.1016/j.compbiomed.2020.103805
resource representing a document's title	COVID-19 detection using deep learning models to exploit Social Mimic Optimization and structured chest X-ray images using fuzzy color and stacking approaches
has PubMed Central identifier	PMC7202857
has PubMed identifier	32568679
schema:publication	Comput Biol Med
resource representing a document's body	covid:601093cf490a2f8e5397feae0e436a0123f13f63#body_text
is schema:about of	named entity 'deep learning' named entity 'https' named entity 'Biology' named entity 'color' named entity 'HTTPS' named entity 'COVID-19' named entity 'exploit' named entity 'Computers' named entity 'stacking' named entity 'Mimic' named entity 'chest X-ray' named entity 'deep learning' named entity 'Computers in Biology and Medicine' named entity 'deep learning' named entity 'chest X-ray' named entity 'virus' named entity 'image processing' named entity 'MERS' named entity 'SqueezeNet' named entity 'Python' named entity 'optimization algorithm' named entity 'deep learning' named entity 'Python' named entity 'CNN' named entity 'SqueezeNet' named entity 'AlexNet' named entity 'image segmentation' named entity 'deep learning-based' named entity 'lungs' named entity 'pneumonia' named entity 'pneumonia' named entity 'pig' named entity 'COVID-19' named entity 'COVID-19' named entity 'deep learning' named entity 'data set' named entity 'COVID-19' named entity 'weakened immune systems' named entity 'COVID-19' named entity 'pneumonia' named entity 'data quality' named entity 'Pneumonia' named entity 'blood group' named entity 'smart devices' named entity 'deep learning' named entity 'SARS' named entity 'runny nose' named entity 'X-ray images' named entity 'RNA' named entity 'deep learning' named entity 'SqueezeNet' named entity 'pneumonia' named entity 'COVID-19' named entity 'University of Montreal' named entity 'Qatar University' named entity 'Iran' named entity 'Image stacking' named entity 'cross-validation' named entity 'algorithm' named entity 'deep learning' named entity 'MATLAB' named entity 'learning rate' named entity 'deep learning' named entity 'pneumonia' named entity 'epidemic' named entity 'pneumonia' named entity 'confusion matrices' named entity 'COVID-19' named entity 'data set' named entity 'Jupyter' named entity 'SVM' named entity 'decision tool' named entity 'single-stranded' named entity 'X-ray images' named entity 'SGD' named entity 'algorithm' named entity 'ReLU function' named entity 'color separation' named entity 'Kaggle'

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