Performance Analysis of Convolutional Neural Network Architectures for the Identification of COVID-19 from Chest X-ray Images
12th IEEE Annual Computing and Communication Workshop and Conference, CCWC 2022
; : 446-452, 2022.
Article
in English
| Scopus | ID: covidwho-1788627
ABSTRACT
The 2019 Novel Coronavirus (COVID-19) has spread quickly over the world and continues to impact the health and well-being of people. The application of deep learning coupled with radiological images is effective for early diagnosis and prevention of the spread. In this study, we introduced a 2D Convolutional Neural Network (CNN) to automatically diagnose Chest X-ray images for multi-class classification (COVID-19 vs. Viral Pneumonia vs. Normal). The objective of the research is to maximize the accuracy of detection by altering various internal parameters of a 2D CNN architecture. A dataset consisting of 1000 COVID-19, 1000 Viral Pneumonia, and 1000 Normal images was considered, and preprocessing steps and augmentation strategies were applied. The training and evaluation of the results were performed on eight 2D CNN architectures with internal parameters changed specifically in each case, and a COVID-19 classification model was proposed. Our proposed computer-aided diagnostic tool produced a significant performance with a classification accuracy of 97.3 %, a sensitivity of 97.3 %, specificity of 98.7%, and precision of 97.4 % on test datasets. These results suggest that it can reliably detect COVID-19 cases and expedite treatment to those in the most need. © 2022 IEEE.
2D Convolutional Neural Network; Coronavirus (COVID-19); Deep Learning; Viral Pneumonia; X-ray Images; Classification (of information); Convolution; Convolutional neural networks; Diagnosis; Network architecture; Chest X-ray image; Convolutional neural network; Coronaviruses; Internal parameters; Neural network architecture; X-ray image; Coronavirus
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Collection:
Databases of international organizations
Database:
Scopus
Language:
English
Journal:
12th IEEE Annual Computing and Communication Workshop and Conference, CCWC 2022
Year:
2022
Document Type:
Article
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