Automatic Detection of Covid-19 from Chest X-Rays using Weighted Average Ensemble Framework - Naresh, P. V.; Visalakshi, R.; Satyanarayana, B. (International Journal of Intelligent Systems and Applications in Engineering, 2023)
COVID-19 stands as one of the most serious diseases resulting from the novel corona virus. Chest x-rays (CXR) have gained widespread recognition as an effective screening tool for lung-related diseases due to their simplicity and cost-effectiveness. Radiologists find CXR image interpretation straightforward and affordable due to its low cost and fast results. In this study, we introduce an innovative approach to improve the detecting and classification of Covid-19 by analyzing their chest x-rays(CXR). Our model includes pretrained architectures like VGG16, ResNet50, and InceptionV3 to create a Weighted Average Ensemble Model. Despite the limitation of dataset our model has achieved an accuracy rate of 98.33%. These results confirm the potential of weighted average ensemble models in contribution to identifying Covid-19 through chest x-ray-based classification.