Coronavirus (COVID-19) Classification Using Deep Features Fusion and Ranking Technique

Abstract

COVID-19, which appeared towards the end of 2019, has become a huge threat to public health. The solution to this threat, which is defined as a global epidemic by the World Health Organization (WHO), is currently undergoing very intensive studies. There is a consensus that the use of Computed Tomography (CT) techniques for early diagnosis of pandemic disease gives both fast and accurate results. This study provides an automated and highly effective method for detecting COVID-19 at an early stage. CT image features are extracted using the convolutional neural network (CNN) architecture, which is the most successful image processing tool of today, for the detection of COVID-19, where early detection is vital for human life. Representation power is increased by combining features from the output of four CNN architectures with data fusion. Finally, the features combined with the feature ranking method are sorted, and their length is reduced. In this way, the dimensional curse is saved. From 150 CT images, 16 × 16 (Subset-1) and 32 × 32 (Subset-2) patches were obtained to create a subset. Within the scope of the proposed method, 3000 patch images are labeled as “COVID-19 (coronavirus)” or “No finding” for use in training and test stages. The Support Vector Machine (SVM) method then classified the processed data. The proposed method shows high performance in Subset-2 with 98.27% accuracy, 98.93% sensitivity, 97.60% specificity, 97.63% sensitivity, 98.28% F1 score and 96.54% Matthews Correlation Coefficient (MCC) metrics. © 2020, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG.