About: Classification of Coronavirus Images using Shrunken Features

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

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
has title	Classification of Coronavirus Images using Shrunken Features
Creator	Barstugan, Mucahid Ozkaya, Umut Ozturk, Saban
Source	MedRxiv
abstract	Necessary screenings must be performed to control the spread of the Corona Virus (COVID-19) in daily life and to make a preliminary diagnosis of suspicious cases. The long duration of pathological laboratory tests and the wrong test results led the researchers to focus on different fields. Fast and accurate diagnoses are essential for effective interventions with COVID-19. The information obtained by using X-ray and Computed Tomography (CT) images is vital in making clinical diagnoses. Therefore it was aimed to develop a machine learning method for the detection of viral epidemics by analyzing X-ray images. In this study, images belonging to 6 situations, including coronavirus images, are classified. Since the number of images in the dataset is deficient and unbalanced, it is more convenient to analyze these images with hand-crafted feature extraction methods. For this purpose, firstly, all the images in the dataset are extracted with the help of four feature extraction algorithms. These extracted features are combined in raw form. The unbalanced data problem is eliminated by producing feature vectors with the SMOTE algorithm. Finally, the feature vector is reduced in size by using a stacked auto-encoder and principal component analysis to remove interconnected features in the feature vector. According to the obtained results, it is seen that the proposed method has leveraging performance, especially in order to make the diagnosis of COVID-19 in a short time and effectively.
has issue date	2020-04-06(xsd:dateTime)
bibo:doi	10.1101/2020.04.03.20048868
has license	medrxiv
sha1sum (hex)	04a548ac23887bab6f10c13545a365b9fdb20803
schema:url	https://doi.org/10.1101/2020.04.03.20048868
resource representing a document's title	Classification of Coronavirus Images using Shrunken Features
resource representing a document's body	covid:04a548ac23887bab6f10c13545a365b9fdb20803#body_text
is schema:about of	named entity 'control' named entity 'X-ray' named entity 'develop' named entity 'stacked' named entity 'algorithm' named entity 'Coronavirus' named entity 'FEATURE' named entity 'VIRAL' named entity 'CASES' named entity 'TO CONTROL' named entity 'REDUCED' named entity 'The' named entity 'feature vectors' named entity 'principal component analysis' named entity 'features' named entity 'images' named entity 'combined' named entity 'number' named entity 'viral' named entity 'According' named entity 'essential' named entity 'study' named entity 'Features' named entity 'feature vector' named entity 'feature vector' named entity 'feature extraction' named entity 'algorithm' named entity 'streptococcus' named entity 'diagnostic method' named entity 'viral epidemic' named entity 'SVM' named entity 'overfitting' named entity 'PCA' named entity 'Fever' named entity 'copyright holder' named entity 'preprint' named entity 'peer-reviewed' named entity 'preprint' named entity 'Covid' named entity 'synthetic data' named entity 'synthetic data' named entity 'COVID' named entity 'WHO' named entity 'data replication' named entity 'pneumonia' named entity 'feature extraction' named entity 'medRxiv' named entity 'preprint' named entity 'CNN' named entity 'PCA' named entity 'medRxiv' named entity 'attenuation' named entity 'medRxiv' named entity 'feature extraction' named entity 'algorithm' named entity 'feature extraction' named entity 'copyright holder' named entity 'COVID' named entity 'preprint' named entity 'algorithm' named entity 'image analysis' named entity 'preprint' named entity 'peer-reviewed' named entity 'copyright holder' named entity 'Early diagnosis' named entity 'SVM' named entity 'peer-reviewed' named entity 'preprint' named entity 'COVID-19 virus' named entity 'preprint' named entity 'COVID-19' named entity 'CNN' named entity 'feature extraction' named entity 'Sars' named entity 'automatic classification' named entity 'peer-reviewed' named entity 'Sars' named entity 'COVID' named entity 'CNN' named entity 'Coronavirus' named entity 'data replication' named entity 'X-ray' named entity 'World Health Organization'

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