A Novel Strategy for COVID-19 Classification from Chest X-ray Images Using Deep Stacked-Ensembles

The issue of COVID-19, increasing notably with a massive mortality rate has led to the WHO declaring it as a pandemic. The unavailability of an antiviral drug has also led to dismay world-wide. The diagnostic tests are performed by collecting samples inserting a swab into the nasal or oral cavity. These collected samples and then sent to laboratories for viral-tests. Recently, chest radiographs are used to observe the proportion of virus in the lungs at various section-scans. As laboratory testing is time-consuming with a lot of human effort, diagnosis using chest radiographs is considered to be a befitting choice. In this research, a stacked-ensemble model is designed to classify whether a patient is infected with COVID-19, viral-pneumonia or has a healthy profile by considering chest X-ray images. A lot of complications were observed from existing literature in classifying COVID-19 radiographic images and are eliminated using our methodology. A training algorithm is constructed to speed up the training process which acquired good generalisations. The proposed model resulted in state-of-the-art outcomes with an accuracy score of 99.48% for binary classification and 97.4% for tri-class classification. Additionally, visualisations are illustrated for a detailed comprehension of the model's perception for the information provided to it.