EnsembleNet: An improved COVID19 Prediction Model using Chest X-Ray Images

Abstract

This paper presents an improved COVID19 prediction model using chest X-Ray images with evolutionary algorithm based ensemble learning. The proposed model uses the transfer learning approach with state-of-the-art pre-trained models for training in isolation. Following the fine-tuning of the models, ensemble of the models is used for inferencing. The weight of the ensemble models are learned by the Differential Evolutional (DE) algorithm. The proposed model exploits the importance of each model in COVID19 inferencing. The proposed model is experimented on COVIDx-CXR2 dataset. Our study shows that the proposed EnsembleNet model outperforms the individual state-of-the-art models in terms of generalization accuracy.