About: Coronavirus (COVID-19) Classification using Deep Features Fusion and Ranking Technique

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About: Coronavirus (COVID-19) Classification using Deep Features Fusion and Ranking Technique Goto Sponge NotDistinct Permalink

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

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type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
title	Coronavirus (COVID-19) Classification using Deep Features Fusion and Ranking Technique
Creator	Barstugan, Mucahid Ozkaya, Umut Ozturk, Saban
source	ArXiv
abstract	Coronavirus (COVID-19) emerged towards the end of 2019. World Health Organization (WHO) was identified it as a global epidemic. Consensus occurred in the opinion that using Computerized Tomography (CT) techniques for early diagnosis of pandemic disease gives both fast and accurate results. It was stated by expert radiologists that COVID-19 displays different behaviours in CT images. In this study, a novel method was proposed as fusing and ranking deep features to detect COVID-19 in early phase. 16x16 (Subset-1) and 32x32 (Subset-2) patches were obtained from 150 CT images to generate sub-datasets. Within the scope of the proposed method, 3000 patch images have been labelled as CoVID-19 and No finding for using in training and testing phase. Feature fusion and ranking method have been applied in order to increase the performance of the proposed method. Then, the processed data was classified with a Support Vector Machine (SVM). According to other pre-trained Convolutional Neural Network (CNN) models used in transfer learning, the proposed method shows high performance on Subset-2 with 98.27% accuracy, 98.93% sensitivity, 97.60% specificity, 97.63% precision, 98.28% F1-score and 96.54% Matthews Correlation Coefficient (MCC) metrics.
has issue date	2020-04-07(xsd:dateTime)
has license	arxiv
sha1sum (hex)	beafcc134bb96b4c1898cbb63be513d951f82b95
resource representing a document's title	Coronavirus (COVID-19) Classification using Deep Features Fusion and Ranking Technique
resource representing a document's body	covid:beafcc134bb96b4c1898cbb63be513d951f82b95#body_text
is schema:about of	named entity 'fusing' named entity 'According' named entity 'training' named entity 'Convolutional Neural Network' named entity 'Computerized Tomography' »more»

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