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Covid-19 detection via deep neural network and occlusion sensitivity maps

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posted on 01.03.2021, 22:18 by Noor Ahmad, Muhammad Aminu, Mohd Halim Mohd Noor
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.

Funding

Ministry of Higher Education Malaysia under the FRGS grant (Acc no: 203.PMATHS.6711942)

History

Email Address of Submitting Author

nooratinah@usm.my

ORCID of Submitting Author

0000-0002-4249-7305

Submitting Author's Institution

Universiti Sains Malaysia

Submitting Author's Country

Malaysia

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