COVID-19 Diagnosis System using SimpNet Deep Model

Authors

Tarza Hasan Abdullah, Department of Computer Science and Information Technology, College of Science, University of Salahaddin Erbil, Erbil, IraqFollow
Fattah Alizadeh, Department of Computer Engineering, School of Science and Engineering, University of Kurdistan Hewler, Erbil, IraqFollow
Berivan Hasan Abdullah, Department of Medicine, Hawler Medical University, Erbil, IraqFollow

Abstract

After the outbreak of COVID-19, immediately it converted from epidemic to pandemic. Radiologic images of CT and X-ray have been widely used to detect COVID-19 disease through observing infrahilar opacity in the lungs. Deep learning has gained popularity in diagnosing many health diseases including COVID-19 and its rapid spreading necessitates the adoption of deep learning in identifying COVID-19 cases. In this study, a deep learning model, based on some principles has been proposed for automatic detection of COVID-19 from X-ray images. The SimpNet architecture has been adopted in our study and trained with X-ray images. The model was evaluated on both binary (COVID-19 and No-findings) classification and multi-class (COVID-19, No-findings, and Pneumonia) classification tasks. Our model has achieved an accuracy value of 98.4% for binary and 93.8% for the multi-class classification. The number of parameters of our model is 11 Million parameters which are fewer than some state-of-the-art methods with achieving higher results.

Keywords

COVID-19, Deep Learning, SimpNet, X-ray Images

Article Type

Article

How to Cite this Article

Abdullah, Tarza Hasan; Alizadeh, Fattah; and Abdullah, Berivan Hasan (2022) "COVID-19 Diagnosis System using SimpNet Deep Model," Baghdad Science Journal: Vol. 19: Iss. 5, Article 6.
DOI: https://doi.org/10.21123/bsj.2022.6074