id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
cord-168974-w80gndka	Ozkaya, Umut	Coronavirus (COVID-19) Classification using Deep Features Fusion and Ranking Technique	2020-04-07		.txt	text/plain	3585	254	59	In this study, a novel method was proposed as fusing and ranking deep features to detect COVID-19 in early phase. 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. 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. When the studies in the literature are examined, Shan et al proposed a neural network model called VB-Net in order to segment the COVID-19 regions in CT images. were able to successfully diagnose COVID-19 using deep learning models that could obtain graphical features in CT images [8] . Deep features were obtained with pre-trained Convolutional Neural Network (CNN) models. In the study, deep features were obtained by using pre-trained CNN networks.	./cache/cord-168974-w80gndka.txt	./txt/cord-168974-w80gndka.txt