COVID_SCREENET: COVID-19 Screening in Chest Radiography Images Using Deep Transfer Stacking

Author:

Elakkiya R.,Vijayakumar PandiORCID,Karuppiah Marimuthu

Publisher

Springer Science and Business Media LLC

Subject

Computer Networks and Communications,Information Systems,Theoretical Computer Science,Software

Reference40 articles.

1. Abdel-Basset, M., Chang, V., & Mohamed, R. (2020). HSMA_WOA: A hybrid novel slime mould algorithm with whale optimization algorithm for tackling the image segmentation problem of chest X-ray images. Applied Soft Computing, 95, 106642.

2. Abdel-Basset, M., Chang, V., & Nabeeh, N. A. (2021a). An intelligent framework using disruptive technologies for COVID-19 analysis. Technological Forecasting and Social Change, 163, 120431.

3. Abdel-Basset, M., Chang, V., Hawash, H., Chakrabortty, R. K., & Ryan, M. (2021b). FSS-2019-nCov: A deep learning architecture for semi-supervised few-shot segmentation of COVID-19 infection. Knowledge-Based Systems, 212, 106647.

4. Alsharif, M. H., Alsharif, Y. H., Albreem, M. A., Jahid, A., Solyman, A. A. A., Yahya, K., Alomari, O. A., & Hossain, M. S. (2020a). Application of machine intelligence technology in the detection of vaccines and medicines for SARS-CoV-2. European Review for Medical and Pharmacological Sciences, 24(22), 11977–11981.

5. Alsharif, M. H., Alsharif, Y. H., Yahya, K., Alomari, O. A., Albreem, M. A., & Jahid, A. (2020b). Deep learning applications to combat the dissemination of COVID-19 disease: A review. European Review for Medical and Pharmacological Sciences, 24(21), 11455–11460.

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