EnsembleNet: An improved COVID19 Prediction Model using Chest X-Ray Images
5th International Conference on Information and Computer Technologies, ICICT 2022
; : 136-140, 2022.
Article
in English
| Scopus | ID: covidwho-2018830
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. © 2022 IEEE.
Full text:
Available
Collection:
Databases of international organizations
Database:
Scopus
Type of study:
Prognostic study
Language:
English
Journal:
5th International Conference on Information and Computer Technologies, ICICT 2022
Year:
2022
Document Type:
Article
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