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International Journal of Advanced Computer Science and Applications(IJACSA), Volume 15 Issue 4, 2024.
Abstract: The constant need for robust and efficient COVID-19 detection methodologies has prompted the exploration of advanced techniques in medical imaging analysis. This paper presents a novel framework that leverages Graph Convolutional Neural Networks (GCNNs) to enhance the detection of COVID-19 from CT scan and X-Ray images. Hence, the GCNN parameters were tuned by the hybrid optimization to gain a more exact detection. Therefore, the novel technique known as Hybrid NADAM Graph Neural Prediction (NAGNP). The framework is designed to achieve efficiency through a hybrid optimization strategy. The methodology involves constructing graph representations from Chest X-ray or CT scan images, where nodes encapsulate critical image patches or regions of interest. These graphs are fed into GCNN architectures tailored for graph-based data, facilitating intricate feature extraction and information aggregation. A hybrid optimization approach is employed to optimize the model's performance, encompassing fine-tuning of GCNN hyperparameters and strategic model optimization techniques. Through rigorous evaluation and validation using diverse datasets, our framework demonstrates promising results in accurate and efficient COVID-19 diagnosis. Integrating GCNNs and hybrid optimization presents a viable pathway toward reliable and practical diagnostic tools in combating the ongoing pandemic.
D. Raghu, Hrudaya Kumar Tripathy and Raiza Borreo, “A Novel Graph Convolutional Neural Networks (GCNNs)-based Framework to Enhance the Detection of COVID-19 from X-Ray and CT Scan Images” International Journal of Advanced Computer Science and Applications(IJACSA), 15(4), 2024. http://dx.doi.org/10.14569/IJACSA.2024.0150473
@article{Raghu2024,
title = {A Novel Graph Convolutional Neural Networks (GCNNs)-based Framework to Enhance the Detection of COVID-19 from X-Ray and CT Scan Images},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2024.0150473},
url = {http://dx.doi.org/10.14569/IJACSA.2024.0150473},
year = {2024},
publisher = {The Science and Information Organization},
volume = {15},
number = {4},
author = {D. Raghu and Hrudaya Kumar Tripathy and Raiza Borreo}
}
Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.