Fast Hybrid Deep Neural Network for Diagnosis of COVID-19 using Chest X-Ray Images

In the last three years, the coronavirus (COVID-19) pandemic put healthcare systems worldwide under tremendous pressure. Imaging techniques, such as Chest X-Ray (CXR) images, play an essential role in diagnosing many diseases (for example, COVID-19). Recently, intelligent systems (Machine Learning (ML) and Deep Learning (DL)) have been widely utilized to identify COVID-19 from other upper respiratory diseases (such as viral pneumonia and lung opacity). Nevertheless, identifying COVID-19 from the CXR images is challenging due to similar symptoms. To improve the diagnosis of COVID-19 using CXR images, this article proposes a new deep neural network model called Fast Hybrid Deep Neural Network (FHDNN). FHDNN consists of various convolutional layers and various dense layers. In the beginning, we preprocessed the dataset, extracted the best features, and expanded it. Then, we converted it from two dimensions to one dimension to reduce training speed and hardware requirements. The experimental results demonstrate that preprocessing and feature expansion before applying FHDNN lead to better detection accuracy and reduced speedy execution. Furthermore, the model FHDNN outperformed the counterparts by achieving an accuracy of 99.9%, recall of 99.9%, F1-Score has 99.9%, and precision of 99.9% for the detection and classification of COVID-19. Accordingly, FHDNN is more reliable and can be considered a robust and faster model in COVID-19 detection.

@article{Ali2023,
  title     = {Fast Hybrid Deep Neural Network for Diagnosis of COVID-19 using Chest X-Ray Images},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {14},
  number    = {3},
  year      = {2023},
  publisher = {The Science and Information Organization},
  author    = {Hussein Ahmed Ali and Nadia Smaoui Zghal and Walid Hariri and Dalenda Ben Aissa},
  doi       = {10.14569/IJACSA.2023.0140364},
  url       = {https://doi.org/10.14569/IJACSA.2023.0140364}
}