  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.
