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International Journal of Advanced Computer Science and Applications(IJACSA), Volume 15 Issue 5, 2024.
Abstract: This research introduces a computer-aided intelligence model designed to automatically identify positive instances of COVID-19 for routine medical applications. The model, built on the Generalized Linear architecture, employs the TwoStep-AS cluster method with diverse screen relatives, Weight sharing and stripping characteristics automatically identify distinctive features in chest X-ray images. Unlike the conventional transformational learning approach, our model underwent training both before and after clustering. The dataset was subjected to a compilation process that involved subdividing samples and categories into multiple sub-samples and subgroups. New cluster labels were then assigned to each cluster, treating each subject cluster as a distinct category. Discriminant features extracted from this process were used to train the Generalized Linear model, which was subsequently applied to classify instances. The TwoStep-AS clustering method underwent modification using pre-compiling the data earlier then employing the Generalized Linear model to identify COVID samples from X-ray chest results. Tests were conducted by the COVID-radiology data guaranteed the correctness of the results. The suggested model demonstrated an impressive accuracy of 90.6%, establishing it as a highly efficient, cost-effective, and rapid intelligence tool for the detection of Coronavirus infections.
Ahmed Hamza Osman, Hani Moetque Aljahdali, Sultan Menwer Altarrazi and Altyeb Taha, “An Integrated Generalized Linear Regression with Two Step-AS Algorithm for COVID-19 Detection” International Journal of Advanced Computer Science and Applications(IJACSA), 15(5), 2024. http://dx.doi.org/10.14569/IJACSA.2024.0150599
@article{Osman2024,
title = {An Integrated Generalized Linear Regression with Two Step-AS Algorithm for COVID-19 Detection},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2024.0150599},
url = {http://dx.doi.org/10.14569/IJACSA.2024.0150599},
year = {2024},
publisher = {The Science and Information Organization},
volume = {15},
number = {5},
author = {Ahmed Hamza Osman and Hani Moetque Aljahdali and Sultan Menwer Altarrazi and Altyeb Taha}
}
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.