Utilisation of Convolutional Neural Networks (CNNS) in the Automated Diagnosis of Covid-19 from Chest X-Ray
Abstract
The COVID-19 pandemic has especially exacerbated the issues faced by the healthcare field, regarding how to ensure rapid and correct infection diagnoses. This study evaluates how Convolutional Neural Networks (CNNs) can be used to automate the diagnosis of COVID-19 using chest X-ray images. The CNN model, as proposed, received training using a publicly available dataset and assessed according to important performance metrics that included accuracy, sensitivity, and specificity. The model accomplished an overall accuracy of 96%, along with a sensitivity of 89% and a specificity of 96%, which points to its strong performance in recognizing COVID-19 cases. The results reveal that diagnostics built on CNN can significantly enhance the use of traditional methods such as PCR tests, supplying quick, reliable, and scalable diagnostic capabilities. Through the addition of AI-enhanced diagnostic capabilities in healthcare processes, the stress on healthcare professionals is lessened by automating image interpretation and quickening patient management. The investigation points out the promise of CNN models in raising diagnostic precision and efficiency in emergent situations, particularly during pandemic outbreaks, and stresses the importance of future research on model generalizability and ethical factors.
Metrics
References
S. M. Anwar, M. Majid, A. Qayyum, M. Awais, M. Alnowami, M. K. Khan, Medical image analysis using convolutional neural networks: a review, Journal of medical systems, 42, (2018) 1-13. https://doi.org/10.1007/s10916-018-1088-1
S. Ganesan, N. Somasiri, R. Jeyavadhanam, G. Karthick, Improved Computational Efficiency of Machine Learning Algorithms Based on Evaluation Metrics to Control the Spread of Coronavirus in the UK. International Journal of Computer and Systems Engineering, 17(10), (2023) 532-537.
T. Ozturk, M. Talo, E.A. Yildirim, U.B. Baloglu, O. Yildirim, U. Rajendra Acharya, Automated detection of COVID-19 cases using deep neural networks with X-ray images, Computers in Biology and Medicine, 121, (2020) 103792. https://doi.org/10.1016/j.compbiomed.2020.103792
E.E.D. Hemdan, A.S. Marwa, M. E. Karar, (2020) COVIDX-Net: A framework of deep learning classifiers to diagnose COVID-19 in X-ray images. arXiv.
D. Singarathnam, S. Ganesan, S. Pokhrel, N. Somasiri, Machine Learning-Based Predictive Models for Cardiovascular Risk Assessment in Data Analysis, Model Development, and Clinical Implications, International Journal of Recent Advances in Multidisciplinary Research, 10(10), (2023) 9084–9089.
H.K. Ahmad, M.R. Milne, Q.D. Buchlak, N. Ektas, G. Sanderson, H. Chamtie, S. Karunasena, J. Chiang, X. Holt, C.H. M. Tang , J.C.Y. Seah, G. Bottrell, N. Esmaili, P. Brotchie, C. Jones, Machine learning augmented interpretation of chest X-rays: a systematic review. Diagnostics, 13(4), (2023) 743. https://doi.org/10.3390/diagnostics13040743
D. K. Gummalla, S. Ganesan, S. Pokhrel, N. Somasiri, Enhanced Early Detection of Thyroid Abnormalities Using a Hybrid Deep Learning Model: A Sequential CNN and K-Means Clustering Approach, Journal of Innovative Image Processing, 6(3), (2024) 244–261. https://doi.org/10.36548/jiip.2024.3.003
M. Alhasan, M. Hasaneen, Digital imaging, technologies and artificial intelligence applications during COVID-19 pandemic, Computerized Medical Imaging and Graphics, 91, (2021) 101933. https://doi.org/10.1016/j.compmedimag.2021.101933
P.G.R. de Almeida, C.D. dos Santos, J.S. Farias, Artificial Intelligence Regulation: a framework for governance, Ethics and Information Technology, 23(3), (2021) 505–525. https://doi.org/10.1007/s10676-021-09593-z
W. Hariri, A. Narin, Deep neural networks for COVID-19 detection and diagnosis using images and acoustic-based techniques: a recent review. Soft computing, 25(24), (2021)15345-15362. https://doi.org/10.1007/s00500-021-06137-x
L. Alzubaidi, J. Zhang, A.J. Humaidi, A. Al-Dujaili, Y. Duan, O. Al-Shamma, M.A. Fadhel, M. Al-Amidie, L. Farhan, Review of deep learning: concepts, CNN architectures, challenges, applications, future directions. Journal of big Data, 8, (2021) 1-74. https://doi.org/10.1186/s40537-021-00444-8
A. Alqahtani, M.M. Zahoor, R. Nasrullah, A. Fareed, Cheema, A. A., Shahrose, A., M. Irfan, A. Alqhatani, A.A. Alsulami, M. Zaffar, S. Rahman, Computer aided covid-19 diagnosis in pandemic era using cnn in chest x-ray images, Life, 12(11), (2022) 1709. https://doi.org/10.3390/life12111709
M.A oun, A.K. Sandhu, Understanding the impact of AI-Driven automation on the workflow of radiologists in emergency care settings. Journal of Intelligent Connectivity and Emerging Technologies, 4(6), (2019) 1-15.
O. Asan, A. Choudhury, Research trends in artificial intelligence applications in human factors health care: mapping review, JMIR human factors, 8(2), (2021) e28236. https://doi.org/10.2196/28236
J.C. Rojas, M. Teran, C.A. Umscheid, Clinician trust in artificial intelligence: What is known and how trust can be facilitated, Critical Care Clinics, 39(4), (2023) 769–782. https://doi.org/10.1016/j.ccc.2023.02.004
L. Awassa, I. Jdey, H. Dhahri, G. Hcini, A. Mahmood, E. Othman, M. Haneef, Study of different deep learning methods for coronavirus (COVID-19) pandemic: Taxonomy, survey and insights. Sensors, 22(5), (2022) 1890. https://doi.org/10.3390/s22051890
I. Barclay, H. Taylor, A. Preece, I. Taylor, D. Verma, G. de Mel, A framework for fostering transparency in shared artificial intelligence models by increasing visibility of contributions, Concurrency and Computation: Practice and Experience, 33(19), (2021) e6129. https://doi.org/10.1002/cpe.6129
C. B. S. Maior, J.M.M. Santana, I.D. Lins, M. J. C. Moura, Convolutional neural network model based on radiological images to support COVID-19 diagnosis: Evaluating database biases, PLoS One, 16(3), (2021) e0247839. https://doi.org/10.1371/journal.pone.0247839
K. Bera, K. A. Schalper, D. L. Rimm, V. Velcheti, A. Madabhushi, Artificial intelligence in digital pathology — new tools for diagnosis and precision oncology, Nature Reviews Clinical Oncology, 16(11), (2019) 703–715. https://doi.org/10.1038/s41571-019-0252-y
S. Campagnano, F. Angelini, G. B. Fonsi, S. Novelli, F.M. Drudi, Diagnostic imaging in COVID-19 pneumonia: a literature review, Journal of Ultrasound, 24(4), (2021) 383–395. https://doi.org/10.1007/s40477-021-00559-x
R.J. Chen, T.Y. Chen, J. Lipkova, J.J. Wang, D.F. Williamson, M.Y. Lu, S. Sahai, F. Mahmood, (2021) Algorithm fairness in ai for medicine and healthcare. arXiv. https://doi.org/10.48550/arXiv.2110.00603
R. Gautam, M. Sharma, Prevalence and diagnosis of neurological disorders using different deep learning techniques: a meta-analysis. Journal of medical systems, 44(2), (2020) 49. https://doi.org/10.1007/s10916-019-1519-7
F. Gama, D. Tyskbo, J. Nygren, J. Barlow, J. Reed, P. Svedberg, Implementation frameworks for artificial intelligence translation into health care practice: Scoping review. Journal of Medical Internet Research, 24(1), (2022) e32215
S. Balasubramaniam, Y. Velmurugan, D. Jaganathan, S. Dhanasekaran, A modified LeNet CNN for breast cancer diagnosis in ultrasound images. Diagnostics, 13(17), (2023) 2746. https://doi.org/10.3390/diagnostics13172746
S. Batra, H. Sharma, W. Boulila, V. Arya, P. Srivastava, M.Z. Khan, M. Krichen, (An intelligent sensor based decision support system for diagnosing pulmonary ailment through standardized chest x-ray scans. Sensors, 22(19), 2022) 7474. https://doi.org/10.3390/s22197474
S. Ganesan, N. Somasiri, Navigating the Integration of Machine Learning in Healthcare: Challenges, Strategies, and Ethical Considerations, Journal of Computational and Cognitive Engineering, (2024) https://orcid.org/0000-0002-6278-2090
Copyright (c) 2024 Qudus Muritala, Swathi Ganesan, Nalinda Somasiri, Ganapathy Kumar
This work is licensed under a Creative Commons Attribution 4.0 International License.
Views: Abstract : 13 | PDF : 8
Plum Analytics
.png){kind=logo}
.png)
.png)
.png)
