An Integrated Generalized Linear Regression with Two Step-AS Algorithm for COVID-19 Detection

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.

@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},
  volume    = {15},
  number    = {5},
  year      = {2024},
  publisher = {The Science and Information Organization},
  author    = {Ahmed Hamza Osman and Hani Moetque Aljahdali and Sultan Menwer Altarrazi and Altyeb Taha},
  doi       = {10.14569/IJACSA.2024.0150599},
  url       = {https://doi.org/10.14569/IJACSA.2024.0150599}
}