Ensemble deep models for covid-19 pandemic classification using chest x-ray images via different fusion techniques - Lamiaa Menshawy; Ahmad H Eid; Rehab F Abdel-Kader (International Journal of Advances in Intelligent Informatics, 2023)
A pandemic epidemic called the coronavirus (COVID-19) has already afflicted people all across the world. Radiologists can visually detect coronavirus infection using a chest X-ray. This study examines two methods for categorizing COVID-19 patients based on chest x-rays: pure deep learning and traditional machine learning. In the first model, three deep learning classifiers' decisions are combined using two distinct decision fusion strategies (majority voting and Bayes optimal). To enhance classification performance, the second model merges the ideas of decision and feature fusion. Using the fusion procedure, feature vectors from deep learning models generate a feature set. The classification metrics of conventional machine learning classifiers were then optimized using a voting classifier. The first proposed model performs better than the second model when it concerns diagnosing binary and multiclass classification. The first model obtains an AUC of 0.998 for multi-class classification and 0.9755 for binary classification. The second model obtains a binary classification AUC of 0.9563 and a multiclass classification AUC of 0.968. The suggested models perform better than both the standard learners and state-of-the-art and state-of-the-art methods.