COVIDiagnosis-Net: Deep Bayes-SqueezeNet based diagnosis of the coronavirus disease 2019 (COVID-19) from X-ray images

By Ferhat Ucar, Deniz Korkmaz. Medical Hypotheses, 2020

692 citations recorded by the source corpus.

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Predicting the trend of indicators related to Covid-19 using the combined MLP-MC model (2021, neutral): “As another future work, the results of this model can be compared with other single or combined models, especially in the ﬁelds of artiﬁcial intelligence and machine learning.”
A combined deep CNN-LSTM network for the detection of novel coronavirus (COVID-19) using X-ray images (2020, neutral): “[24] proposed a COVID-19 detection system based on deep architecture from X-ray images.”
Automatic detection of coronavirus disease (COVID-19) using X-ray images and deep convolutional neural networks (2020, methodology): “They worked with 76 COVID-19 data [25].”
Can AI Help in Screening Viral and COVID-19 Pneumonia? (2020, methodology): “[51] applied transfer learning approach on 310 normal, 330 bacterial pneumonia, 327 viral pneumonia and 284 COVID-19 pneumonia images.”
Deep learning approaches for COVID-19 detection based on chest X-ray images (2020, methodology): “Ucar and Korkmaz (2020) used deep Bayes- A.M. Ismael and A. Şengür SqueezeNet for COVID-19 detection based on chest X-ray images, and reported accuracy as being 76.37% for the multiclass case.”