Computer‐aided diagnosis of COVID‐19 disease from chest X‐ray images integrating deep feature extraction. Issue 5 (9th December 2021)

• Record Type: Journal Article
• Authors: Aziz, Sumair; Khan, Muhammad Umar; Rehman, Abdul; Tariq, Zain; Iqtidar, Khushbakht
• Other Names: Sharma Rohit guestEditor.; Gupta Deepak guestEditor.; Maseleno Andino guestEditor.; Peng Sheng‐Lung guestEditor.; Menon Varun G. guestEditor.; Khosravi Reza guestEditor.; Jolfaei Alireza guestEditor.; Kumar Akshi guestEditor.; P Vinod guestEditor.
• Is Part Of: Expert systems. Volume 39:Issue 5(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2021-12-09
• Subjects: computer aided diagnosis -- coronavirus -- COVID‐19 -- deep feature fusion -- deep transfer learning -- K‐nearest neighbour -- medical image analysis -- support vector machines; Expert systems (Computer science); 006.33
• DOI: 10.1111/exsy.12919
• Languages: English
• ISSNs: 0266-4720

Abstract

Abstract: The novel coronavirus (COVID‐19) has an enormous impact on the daily lives and health of people residing in more than 200 nations. This article proposes a deep learning‐based system for the rapid diagnosis of COVID‐19. Chest x‐ray radiograph images were used because recent findings revealed that these images contain salient features about COVID‐19 disease. Transfer learning was performed using different pre‐trained convolutional neural networks models for binary (normal and COVID‐19) and triple (normal, COVID‐19 and viral pneumonia) class problems. Deep features were extracted from a fully connected layer of the ResNET50v2 model and feature dimension was reduced through feature reduction methods. Feature fusion of feature sets reduced through analysis of variance (ANOVA) and mutual information feature selection (MIFS) was fed to Fine K‐nearest neighbour to perform binary classification. Similarly, serial feature fusion of MIFS and chi‐square features were utilized to train Medium Gaussian Support Vector Machines to distinguish normal, COVID‐19 and viral pneumonia cases. The proposed framework yielded accuracies of 99.5% for binary and 95.5% for triple class experiments. The proposed model shows better performance than the existing methods, and this research has the potential to assist medical professionals to enhance the diagnostic ability to detect coronavirus disease.