Enhancing COVID-19 Classification in Chest X-Rays through DualChannel Graph-Based Neural Networks
Keywords:
COVID-19 classification, dual-channel graph convolutional neural network, medical diagnosis, deep learning, lung opacity, pneumonia, automated tool, healthcare.Abstract
The classification of COVID-19 from chest X-ray (CXR) images is essential for rapid and accurate diagnosis, which can significantly aid in the effective management and treatment of the disease. This application is crucial for early detection, reducing the strain on healthcare systems, guiding clinical decisions, and enhancing patient outcomes. Existing challenges include variability in image quality, the presence of overlapping symptoms with other lung conditions, limited availability of labeled datasets, and the need for highly specialized expertise for accurate interpretation. The proposed methodology involves an advanced image preprocessing technique to enhance CXR images, followed by the utilization of a dual-channel graph convolutional neural network (GCN) for classification. This approach leverages the power of GCNs to capture complex patterns and relationships in medical images. The dual-channel aspect allows the model to process and integrate features from both spatial and frequency domains, improving its ability to differentiate between COVID-19, normal, pneumonia viral, and lung opacity classes. The preprocessing step includes noise reduction, contrast enhancement, and normalization to ensure uniformity across the dataset. This sophisticated methodology aims to overcome existing diagnostic challenges and provide a robust, automated tool for accurate CXR image classification.
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References
Y. Fang, H. Zhang, J. Xie, M. Lin, L. Ying, P. Pang, et al., Sensitivity of chest CT for COVID-19:
comparison to RT-PCR, Radiology 296 (2020) E115–E117.
X. Xie, Z. Zhong, W. Zhao, C. Zheng, F. Wang, J. Liu, Chest CT for typical coronavirus disease 2019
(COVID-19) pneumonia: relationship to negative RT-PCR testing, Radiology 296 (2020) E41–E45.
S. Inui, A. Fujikawa, M. Jitsu, N. Kunishima, S. Watanabe, Y. Suzuki, et al., Chest CT findings in cases
from the cruise ship diamond princess with coronavirus disease (COVID-19), Radiol.: Cardiothorac.
Imaging 2 (2020) e200110.
R. Jain, M. Gupta, S. Taneja, D.J. Hemanth, Deep learning based detection and analysis of COVID-19
on chest X-ray images, Appl. Intell. 51 (2021) 1690–1700.
B. Wang, S. Jin, Q. Yan, H. Xu, C. Luo, L. Wei, et al., AI-assisted CT imaging analysis for COVID-19
screening: building and deploying a medical AI system, Appl. Soft. Comput. 98 (2021) 106897.
X. Xu, X. Jiang, C. Ma, P. Du, X. Li, S. Lv, et al., A deep learning system to screen novel coronavirus
disease 2019 pneumonia, Engineering 6 (2020) 1122–1129.
Y. Song, S. Zheng, L. Li, X. Zhang, X. Zhang, Z. Huang, Y. Yang, Deep learning enables accurate
diagnosis of novel coronavirus (COVID-19) with CT images, IEEE/ACM Trans. Comput. Biol.
Bioinform. 18 (6) (2021) 2775–2780.
F. Zheng, L. Li, X. Zhang, Y. Song, Z. Huang, Y. Chong, et al., Accurately discriminating COVID-19
from viral and bacterial pneumonia according to CT images via deep learning, Interdiscip. Sci.: Comput.
Life Sci. 13 (2021) 273–285.
C. Zheng, X. Deng, Q. Fu, Q. Zhou, J. Feng, H. Ma, et al., Deep learning-based detection for COVID-
from chest CT using weak label, MedRxiv (2020), 2020.03.12.20027185.
Y. Pathak, P.K. Shukla, A. Tiwari, S. Stalin, S. Singh, Deep transfer learning based classification model
for COVID-19 disease, Irbm 43 (2022) 87–92.
U. Ozkaya, S. Oztürk, M. Barstugan, Coronavirus (COVID-19) classification using deep features fusion
and ranking technique. Big Data Analytics and Artificial Intelligence Against COVID-19: Innovation
Vision and Approach, Springer Nature, 2020, pp. 281–295.
A. Amyar, R. Modzelewski, H. Li, S. Ruan, Multi-task deep learning based CT imaging analysis for
COVID-19 pneumonia: classification and segmentation, Comput. Biol. Med. 126 (2020) 104037.
M. Polsinelli, L. Cinque, G. Placidi, A light CNN for detecting COVID-19 from CT scans of the chest,
Pattern. Recognit. Lett. 140 (2020) 95–100.
E. Luz, P. Silva, R. Silva, L. Silva, J. Guimarães, G. Miozzo, et al., Towards an effective and efficient
deep learning model for COVID-19 patterns detection in X-ray images, in: Research on Biomedical
Engineering Springer, 2021, pp. 1–14.
A. Al Smadi, A. Abugabah, A.M. Al-Smadi, S. Almotairi, SEL-COVIDNET: an intelligent application
for the diagnosis of COVID-19 from chest X-rays and CT-scans, Inf. Med. Unlock 32 (2022) 101059.
A. Al Smadi, A. Abugabah, S.F. Mahenge, F. Shahid, Information systems in medical settings: a COVID-
detection system using X-ray scans, in: 2022 26th International Computer Science and Engineering
Conference (ICSEC), 2022, pp. 72–77.
A. Mehmood, A. Abugabah, A. Al Smadi, Smart health care system for early detection of COVID-19
using X-ray scans, in: 2022 International Conference on Electrical, Computer and Energy Technologies
(ICECET), 2022, pp. 1–4.
A. Mehmood, A. Abugabah, A.A. Smadi, R. Alkhawaldeh, An intelligent information system and
application for the diagnosis and analysis of COVID-19, in: Intelligent Computing & Optimization:
Proceedings of the 4th International Conference on Intelligent Computing and Optimization 2021
(ICO2021) 3, 2022, pp. 391–396.
K. Gupta, V. Bajaj, Deep learning models-based CT-scan image classification for automated screening
of COVID-19, Biomed. Signal Process Control 80 (2023) 104268.
N. Ullah, J.A. Khan, S. El-Sappagh, N. El-Rashidy, M.S. Khan, A holistic approach to identify and
classify COVID-19 from chest radiographs, ECG, and CT-scan images using shufflenet convolutional
neural network, Diagnostics 13 (2023) 162.
A. Agnihotri, N. Kohli, Challenges, opportunities, and advances related to COVID-19 classification
based on deep learning, Data Sci. Manag. 6 (2023) 98–109
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