COVID-19 Disease Detection based on X-Ray Image Classification using CNN with GEV Activation Function

The globe was rocked by unprecedented levels of disruption, which had devastating effects on daily life, global health, and global economy. Since the COVID-19 epidemic started, methods for delivering accurate diagnoses for multi-category classification have been proposed in this work (COVID vs. normal vs. pneumonia). XceptionNet and Dense Net, two transfer learning pre-trained model networks, are employed in our CNN model. The low-level properties of the two DCNN structures were combined and used to a classifier for the final prediction. To get better results with unbalanced data, we used the GEV activation function (generalized extreme value) to augment the training dataset using data augmentation for validation accuracy, which allowed us to increase the training dataset while still maintaining validation accuracy with the output classifier. The model has been put through its paces in two distinct scenarios. In the first instance, the model was tested using Image Augmentation for train data and the GEV (generalized extreme value) function for output class, and it got a 94% accuracy second instance Model evaluations were conducted without data augmentation and yielded an accuracy rating of 95% for the output class.

@article{Mohamed2022,
  title     = {COVID-19 Disease Detection based on X-Ray Image Classification using CNN with GEV Activation Function},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {13},
  number    = {9},
  year      = {2022},
  publisher = {The Science and Information Organization},
  author    = {Karim Ali Mohamed and Emad Elsamahy and Ahmed Salem},
  doi       = {10.14569/IJACSA.2022.01309103},
  url       = {https://doi.org/10.14569/IJACSA.2022.01309103}
}