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.
【초록키워드】 COVID-19, deep learning, Pneumonia, Tuberculosis, knowledge, Diagnosis, Symptom, Infectious disease, cough, Viral pneumonia, X-ray, Viral, Measures, Accuracy, Fever, Algorithm, Convolutional neural network, severe cases, chest X-ray, dataset, respiratory, medical diagnosis, Chest X-ray images, best, Support System, Support, Chest X-ray image, transfer, Severe case, Radiographic, robustness, diagnosing, approach, detect, develop, evaluate, was collected, disease-causing, patients with COVID-19, 【제목키워드】 COVID-19, learning, novel, Image, technique, System,