Designing an improved deep learning-based model for COVID-19 recognition in chest X-ray images: a knowledge distillation approach
Author:
Publisher
Springer Science and Business Media LLC
Subject
General Earth and Planetary Sciences,General Environmental Science
Link
https://link.springer.com/content/pdf/10.1007/s42044-023-00167-4.pdf
Reference51 articles.
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2. de la Torre, J., Valls, A., Puig, D.: A deep learning interpretable classifier for diabetic retinopathy disease grading. Neurocomputing 396, 465–476 (2020). https://doi.org/10.1016/J.NEUCOM.2018.07.102
3. Gupta, A., Anjum, S.G., Katarya, R.: InstaCovNet-19: a deep learning classification model for the detection of COVID-19 patients using chest X-ray. Appl. Soft Comput.Comput. (2021). https://doi.org/10.1016/J.ASOC.2020.106859
4. Ouchicha, C., Ammor, O., Meknassi, M.: CVDNet: a novel deep learning architecture for detection of coronavirus (Covid-19) from chest X-ray images. Chaos Solitons Fractals (2020). https://doi.org/10.1016/J.CHAOS.2020.110245
5. Saha, P., Sadi, M.S., Islam, M.M.: EMCNet: automated COVID-19 diagnosis from X-ray images using convolutional neural network and ensemble of machine learning classifiers. Inform. Med. (2021). https://doi.org/10.1016/J.IMU.2020.100505
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