An Augmentation-Based System for Diagnosing COVID-19 Using Deep Learning

Recently, due to the dangerous spread of COVID-19, there has been strong competition among computer science researchers within the scientific research community to employ deep learning for the development of intelligent medical systems that diagnose this illness. Enhancing accuracy is considered the most important objective, and augmentation techniques are used in this context. This study addresses two main issues related to applying augmentation on X-ray and CT-scan images: losing the positional information of augmented medical images and the integration of extracted features while scanning them. The use of the Vision Transformer Structure, supported by a Position-Aware Embedding (PAE) method, is proposed to deal with these issues. Moreover, in this study, a student–teacher-based approach was adopted to enable considerable resistance against training on a small batch of training images. Due to the sensitivity of medical data, preserving the privacy of patients was taken into account by using a pseudonym-based anonymity approach. After evaluations based on accuracy, precision, recall, and specificity metrics, the results showed that the proposed system has a high-level capability to predict class images (X-ray or CT-scan) as well as considerable resistance against training on small medical images.

@article{Alrahhal2025,
  title     = {An Augmentation-Based System for Diagnosing COVID-19 Using Deep Learning},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {16},
  number    = {8},
  year      = {2025},
  publisher = {The Science and Information Organization},
  author    = {Mohamad Shady Alrahhal and Mohammad A. Mezher and Osamah A.M. Ghaleb and Mohammad Al-Hjouj and Raghad Sehly and Samir Bataineh},
  doi       = {10.14569/IJACSA.2025.0160819},
  url       = {https://doi.org/10.14569/IJACSA.2025.0160819}
}