COVID-19 Detection from X-Ray Images using Convoluted Neural Networks: A Literature Review

This paper reviews a host of other peer-reviewed articles related to the detection of COVID-19 infection from X-ray images using Convoluted Neural Network (CNN) approaches. It stems from a background of a pandemic that has hit the world and negatively affected all spheres of life. The currently available testing mechanisms are invasive, expensive, time-consuming, and not everywhere. The paper considered 33 main articles supported by several other articles. The measurement metrics considered in this review are accuracy, precision, recall, F1-score, and specificity. The inclusion criteria for studies was that the article should have been written after the pandemic began, deliberates on CNN, and attempts to detect the disease from X-ray images. Findings suggest that transfer learning, support vector machines, long short-term memory, and other CNN approaches are highly effective in predicting the likelihood of the disease from X-rays. However, multi-class predictions seemed to score lowly on the accuracy score relative to their binary counterparts. Also, data augmentation significantly improved the performance of the models. Hence, the paper concluded that all reviewed approaches are effective. Recommendations are that analysts should integrate transfer learning procedures in the model formulation process, engage in data augmentation practices, and focus on classifying data based on binary classes.

@article{Alrusaini2022,
  title     = {COVID-19 Detection from X-Ray Images using Convoluted Neural Networks: A Literature Review},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {13},
  number    = {3},
  year      = {2022},
  publisher = {The Science and Information Organization},
  author    = {Othman A. Alrusaini},
  doi       = {10.14569/IJACSA.2022.0130313},
  url       = {https://doi.org/10.14569/IJACSA.2022.0130313}
}