Deep learning model for binary classification of COVID-19 based on Chest X-Ray
15th International Conference on Developments in eSystems Engineering, DeSE 2023
; 2023-January:45-49, 2023.
Article
in English
| Scopus | ID: covidwho-2325981
ABSTRACT
COVID-19 is a novel virus infecting the upper respiratory tract and lungs. On a scale of the global pandemic, the number of cases and deaths had been increasing each day. Chest X-ray (CXR) images proved effective in monitoring a variety of lung illnesses, including the COVID-19 disease. In recent years, deep learning (DL) has become one of the most significant topics in the computing world and has been extensively applied in several medical applications. In terms of automatic diagnosis of COVID-19, those approaches had proven to be very effective. In this research, a DL technology based on convolution neural networks (CNN) models had been implemented with less number of layers with tuning parameters that will take less time for training for binary classification of COVID-19 based on CXR images. Experimental results had shown that the proposed model for training had achieved an accuracy of 96.68%, Recall of 94.12%, Precision of 93.49%, Specificity of 97.61%, and F1 Score of 93.8%. Those results had shown the high value of utilizing DL for early COVID-19 diagnosis, which can be utilized as a useful tool for COVID-19 screening. © 2023 IEEE.
Chest X-Ray; Convolutional neural network; COVID-19; Deep learning; Disease detection; Medical application; Medical images; Convolution; Convolutional neural networks; Diagnosis; Image classification; Medical applications; Medical imaging; Multilayer neural networks; Viruses; Automatic diagnosis; Binary classification; Chest X-ray image; Learning models; Medical image; Upper respiratory tract
Full text:
Available
Collection:
Databases of international organizations
Database:
Scopus
Type of study:
Diagnostic study
/
Prognostic study
Language:
English
Journal:
15th International Conference on Developments in eSystems Engineering, DeSE 2023
Year:
2023
Document Type:
Article
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