COVIDetectioNet: COVID-19 diagnosis system based on X-ray images using features selected from pre-learned deep features ensemble

Abstract

The recent novel coronavirus (also known as COVID-19) has rapidly spread worldwide, causing an infectious respiratory disease that has killed hundreds of thousands and infected millions. While test kits are used for diagnosis of the disease, the process takes time and the test kits are limited in their availability. However, the COVID-19 disease is also diagnosable using radiological images taken through lung X-rays. This process is known to be both faster and more reliable as a form of identification and diagnosis. In this regard, the current study proposes an expert-designed system called COVIDetectioNet model, which utilizes features selected from combination of deep features for diagnosis of COVID-19. For this purpose, a pretrained Convolutional Neural Network (CNN)-based AlexNet architecture that employed the transfer learning approach, was used. The effective features that were selected using the Relief feature selection algorithm from all layers of the architecture were then classified using the Support Vector Machine (SVM) method. To verify the validity of the model proposed, a total of 6092 X-ray images, classified as Normal (healthy), COVID-19, and Pneumonia, were obtained from a combination of public datasets. In the experimental results, an accuracy of 99.18% was achieved using the model proposed. The results demonstrate that the proposed COVIDetectioNet model achieved a superior level of success when compared to previous studies.

COVID-19 Detection System Using Chest CT Images and Multiple Kernels-Extreme Learning Machine Based on Deep Neural Network

“…In their experimental results, the highest accuracy score achieved was 98% with the ResNet50 model. Similar to these studies, there are other research studies conducted that are based on Deep CNNs using X-ray images for the classification of COVID-19 disease [13] , [14] , [15] , [16] , [17] , [18] .…”

COVID-19 Recognition Using Ensemble-CNNs in Two New Chest X-ray Databases

“…Most of the literature studies have dealt with the recognition of two or three classes of COVID-19-related diseases using initially small databases [ 1 , 9 , 13 , 14 , 22 ]. In our work, two scenarios are investigated to distinguish COVID-19 infection from other Lung diseases.…”

“…M. Turkoglu proposed the COVIDetectioNet [ 1 ] framework, which consists of three steps. First, the pre-trained AlexNet architecture [ 25 ] is used with transfer learning.…”

“…Reverse Transcription Polymerase Chain Reaction (RT-PCR) is the definitive test for the recognition of COVID-19 disease. However, RT-PCR test is a time-consuming, laborious, and complicated manual process [ 1 ]. In addition, test kits are only available in limited numbers worldwide [ 1 ].…”

“…However, RT-PCR test is a time-consuming, laborious, and complicated manual process [ 1 ]. In addition, test kits are only available in limited numbers worldwide [ 1 ]. On the other hand, the rate of false negatives varies depending on how long the infection has been present.…”