Covid-19 detection via deep neural network and occlusion sensitivity maps
Alexandria Engineering Journal
; 2021.
Article
in English
| ScienceDirect | ID: covidwho-1157078
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.
Full text:
Available
Collection:
Databases of international organizations
Database:
ScienceDirect
Type of study:
Diagnostic study
Language:
English
Journal:
Alexandria Engineering Journal
Year:
2021
Document Type:
Article
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