sis 22(6): e8

Research Article

Chest X-ray and CT Scan Classification using Ensemble Learning through Transfer Learning

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  • @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
Salman Ahmad Siddiqui1,*, Neda Fatima1, Anwar Ahmad1
  • 1: Jamia Millia Islamia
*Contact email: salman007.rec@gmail.com

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.