COVID-19 and Other Lung Disease Detection Using VGG19 Pretrained Features and Support Vector Machine

At present, pandemic phase is declared by World Health Organization caused by COVID-19 disease that endangers all walks of life. The disease has spread quickly around the world causing many countries to lockdown. The medical center could not handle a large number of infected patients. To effectively and automatically classify the infected patients is a big challenge. So, we introduce an efficient lung disease detection method that can detect and identify normal people (without lung disease) and others who have lung disease(s) using chest X-ray images. We consider many well-known lung diseases which are COVID-19, Pneumonia, Pneumothorax, and Atelectasis. First, we preprocessed the images and performed feature extraction using a VGG19 deep-learning model, and then used a Support Vector Machine as the classification model. The dataset that we used is publicly provided with many types of diseases. Our model obtains great results on binary class classification considering COVID-19 and non-COVID-19 classes with 99.0% accuracy, 98.3% recall, 99.1% precision, and 98.7% f1-score. With multi-class classification (5 output classes), we obtain 99.2% accuracy for COVID-19 detection, 99.2% accuracy for Pneumonia, 85.4% accuracy for Atelectasis, and 84.8% accuracy for Pneumothorax.