Bioinspired CNN Approach for Diagnosing COVID-19 Using Images of Chest X-Ray
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Publisher
Springer International Publishing
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-20541-5_8
Reference24 articles.
1. Singh, A. K., Kumar, A., Mufti Mahmud, M., Kaiser, S., & Kishore, A. (2021). COVID-19 infection detection from chest X-ray images using hybrid social group optimization and support vector classifier. Cognitive Computation, 1–13.
2. Dhiman, G., Chang, V., Singh, K. K., & Shankar, A. (2021). Adopt: Automatic deep learning and optimization-based approach for detection of novel coronavirus covid-19 disease using x-ray images. Journal of Biomolecular Structure and Dynamics, 1–13.
3. Anter, A. M., Oliva, D., Thakare, A., & Zhang, Z. (2021). AFCM-LSMA: New intelligent model based on Lévy slime mould algorithm and adaptive fuzzy C-means for identification of COVID-19 infection from chest X-ray images. Advanced Engineering Informatics, 49, 101317.
4. Altan, A., & Karasu, S. (2020). Recognition of COVID-19 disease from X-ray images by hybrid model consisting of 2D curvelet transform, chaotic salp swarm algorithm and deep learning technique. Chaos, Solitons & Fractals, 140, 110071.
5. Elaziz, M. A., Hosny, K. M., Salah, A., Darwish, M. M., Songfeng, L., & Sahlol, A. T. (2020). New machine learning method for image-based diagnosis of COVID-19. PLoS One, 15(6), e0235187.
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