Research Article
Chest X-ray and CT Scan Classification using Ensemble Learning through Transfer Learning
@ARTICLE{10.4108/eetsis.vi.382, author={Salman Ahmad Siddiqui and Neda Fatima and Anwar Ahmad}, title={Chest X-ray and CT Scan Classification using Ensemble Learning through Transfer Learning}, journal={EAI Endorsed Transactions on Scalable Information Systems}, volume={9}, number={6}, publisher={EAI}, journal_a={SIS}, year={2022}, month={6}, keywords={COVID-19, Ensemble learning, X-ray, Transfer Learning}, doi={10.4108/eetsis.vi.382} }- Salman Ahmad Siddiqui
Neda Fatima
Anwar Ahmad
Year: 2022
Chest X-ray and CT Scan Classification using Ensemble Learning through Transfer Learning
SIS
EAI
DOI: 10.4108/eetsis.vi.382
Abstract
COVID-19 has posed an extraordinary challenge to the entire world. As the number of COVID-19 cases continues to climb around the world, medical experts are facing an unprecedented challenge in correctly diagnosing and predicting the disease. The present research attempts to develop a new and effective strategy for classifying chest X-rays and CT Scans in order to distinguish COVID-19 from other diseases. Transfer learning was used to train various models for chest X-rays and CT Scan, including Inceptionv3, Xception, InceptionResNetv2, DenseNet121, and Resnet50. The models are then integrated using an ensemble technique to improve forecast accuracy. The proposed ensemble approach is more effective in classifying X-ray and CT Scan and forecasting COVID-19.
Copyright © 2022 Salman Ahmad Siddiqui et al., licensed to EAI. This is an open access article distributed under the terms of the Creative Commons Attribution license, which permits unlimited use, distribution and reproduction in any medium so long as the original work is properly cited.