Covid-19 detection via deep neural network and occlusion sensitivity
maps
- Noor Ahmad ,
- Muhammad Aminu ,
- Mohd Halim Mohd Noor
Abstract
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.