Deep Viewing for Covid-19 Detection from X-Ray Using CNN Based Architecture
2021 8th International Conference on Electrical Engineering, Computerscience and Informatics (Eecsi) 2021
; : 186-191, 2021.
Article
in English
| Web of Science | ID: covidwho-2040844
ABSTRACT
The Covid-19 coronavirus has turned into a serious, life-threatening disease that is prevalent worldwide as it is most likely to infect. An automated protocol system is a compelling idea to stop the spread of covid19. This article aims at a deep learning model supported by a convolutional neural network (CNN) to facilitate automatic diagnosis from chest X-rays. A collection of 2875 covid19 images and 10293 X-ray pictures to recognize covid19 counts is being used as the data set for the drafting. From the experimental results, it can be seen that the proposed structure achieves 96% specificity, 97% AUC 96% accuracy, 96% sensitivity, and 96% F1-score. Therefore, the results of the proposed system will help clinicians and researchers discover COVID-19 patients and facilitate the treatment of COVID-19 patients.
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Collection:
Databases of international organizations
Database:
Web of Science
Language:
English
Journal:
2021 8th International Conference on Electrical Engineering, Computerscience and Informatics (Eecsi) 2021
Year:
2021
Document Type:
Article
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