COVIDetectioNet: COVID-19 diagnosis system based on X-ray images using features selected from pre-learned deep features
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COVIDetectioNet: COVID-19 diagnosis system based on X-ray images using features selected from pre-learned deep features ensemble Muammer Turkoglu 1
# Springer Science+Business Media, LLC, part of Springer Nature 2020
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. Keywords COVID-19 diagnosis . Relief algorithm . SVM . Pre-learned features . CNN
1 Introduction The rapid spread of the coronavirus infection, known as COVID-19 has resulted in an extensive health, financial, and personal impact on a global scale. The virus was first identified in Wuhan, China, in December 2019. Later, the COVID19 virus has been found to be easily transmitted from person to person, resulting in a pandemic affecting almost all countries and territories worldwide. With each passing day, hundreds of deaths and thousands of new infections are being recorded in many different countries [1, 2], and at the time this paper was penned, the global total number of COVID-19 cases has stood at approximately 3.5 million confirmed cases and 224,300 fatalities across 205 countries [3]. Some of the people who have contracted the COVID-19 disease suffer medical complications such as acute respiratory disorders and secondary infections. In such severe cases, early * Muammer Turkoglu [email protected] 1
Computer Engineering Department, Engineering Faculty, Bingol University, 12000 Bingol, Turkey
treatment carried out following early diagnosis plays a critical role in reducing the likelihood of mortality. Currently, the main method used to detect COVID-19 disease is a reverse transcriptase-polymerase chain reaction (RT-PCR), which is a test conduc
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