Covid-19 detection via deep neural network and occlusion sensitivity maps
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Deep learning approaches have attracted a lot of attention in the automatic detection of Covid-19 and transfer learning is the most common approach. However, majority of the pre-trained models are trained on color images, which can cause inefficiencies when fine-tuning the models on Covid-19 images which are often grayscale. To address this issue, we propose a deep learning architecture called CovidNet which requires a relatively smaller number of parameters. CovidNet accepts grayscale images as inputs and is suitable for training with limited training dataset. Experimental results show that CovidNet outperforms other state-of-the-art deep learning models for Covid-19 detection.
2021 ◽
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2021 ◽
Vol 10
(4)
◽
pp. 2821-2829
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2020 ◽
Vol 30
(05)
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pp. 649-668
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2014 ◽
Vol 641-642
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pp. 1287-1290
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