COVID-19 Detection via Image Classification using Deep Learning on Chest X-Ray
2021 Ethics and Explainability for Responsible Data Science Conference, EE-RDS 2021
; 2021.
Article
in English
| Scopus | ID: covidwho-1741177
ABSTRACT
The focus of this study is to evaluate and examine a set of deep learning transfer learning techniques applied to chest radiograph images for the classification of COVID-19, normal (healthy), and pneumonia. In this work, we have used four transfer learning models, VGG16, InceptionV3, ResNet50, and DenseNet121 for the classification tasks. Our results indicate that the VGG16 method outperforms comparative classification models in terms of accuracy, sensitivity, and specificity. The VGG16 model detects and classifies COVID-19, normal (healthy), and pneumonia with 94% test accuracy, 94% sensitivity, and 94.20% specificity. Code is publically available at https//github.com/ayyaz-azeem/Covid19challenge.git © 2021 IEEE.
Full text:
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Collection:
Databases of international organizations
Database:
Scopus
Language:
English
Journal:
2021 Ethics and Explainability for Responsible Data Science Conference, EE-RDS 2021
Year:
2021
Document Type:
Article
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