Abstract
COVID-19 is an infectious disease-causing flu-like respiratory problem with various symptoms such as cough or fever, which in severe cases can cause pneumonia. The aim of this paper is to develop a rapid and accurate medical diagnosis support system to detect COVID-19 in chest X-ray images using a stacking approach combining transfer learning techniques and KNN algorithm for selection of the best model. In deep learning, we have multiple approaches for building a classification system for analyzing radiographic images. In this work, we used the transfer learning technique. This approach makes it possible to store and use the knowledge acquired from a pretrained convolutional neural network to solve a new problem. To ensure the robustness of the proposed system for diagnosing patients with COVID-19 using X-ray images, we used a machine learning method called the stacking approach to combine the performances of the many transfer learning-based models. The generated model was trained on a dataset containing four classes, namely, COVID-19, tuberculosis, viral pneumonia, and normal cases. The dataset used was collected from a six-source dataset of X-ray images. To evaluate the performance of the proposed system, we used different common evaluation measures. Our proposed system achieves an extremely good accuracy of 99.23% exceeding many previous related studies.
Highlights
Introduction e SARSCoV-2 has caused the borders of many countries to be closed and millions of citizens to be confined to their homes due to infection rates; there have been more than 147 million confirmed cases worldwide at this time (April 25, 2021). is virus originated in China in December 2019
COVID-19 is an infectious disease-causing flu-like respiratory problem with various symptoms such as cough or fever, which in severe cases can cause pneumonia. e aim of this paper is to develop a rapid and accurate medical diagnosis support system to detect COVID-19 in chest X-ray images using a stacking approach combining transfer learning techniques and KNN algorithm for selection of the best model
We aim to improve the prediction accuracy of COVID-19. us, the proposed system combines five transfer learning (TL) algorithms, namely, ResNet152V2, ResNet101V2, MobileNetV2, VGG16, and VGG19. ese TL-based models automatically extract the radiographic images’ features. en, we implemented the stacking technique and the KNN algorithm to combine the performances of the five generated classifiers models and make the final prediction
Summary
Introduction e SARSCoV-2 has caused the borders of many countries to be closed and millions of citizens to be confined to their homes due to infection rates; there have been more than 147 million confirmed cases worldwide at this time (April 25, 2021). is virus originated in China in December 2019. CoV-2 has caused the borders of many countries to be closed and millions of citizens to be confined to their homes due to infection rates; there have been more than 147 million confirmed cases worldwide at this time (April 25, 2021). Is virus originated in China in December 2019. China succeeded in containing the virus for almost three months after the start of the crisis. Clinical features of infected COVID-19 cases include fever, respiratory symptoms, cough, dyspnea, and viral pneumonia [3]. E COVID-19 test is based on taking samples from the respiratory tract [4]. A high number of tests may prove to be the key tool to stop the virus spread in some countries. It is important to find and develop alternative methods to perform these tests quickly and efficiently
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