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A Deep Neural Network to Distinguish COVID-19 from other Chest Diseases Using X-ray Images


Saleh Albahli*   Pages 1 - 11 ( 11 )


Background: Scanning patient’s lungs to detect a Coronavirus 2019 (COVID-19) may lead to similar imaging with other chest diseases that strongly requires a multidisciplinary approach to confirm the diagnosis. There are only few works targeted pathological x-ray images. Most of the works targeted only single disease detection which is not good enough. Some works have provided for all classes however the results suffer due to lack of data for rare classes and data unbalancing problem.

Methods: Due to arise of COVID-19 virus medical facilities of many countries are overwhelmed and there is a need of intelligent system to detect it. There have been few works regarding detection of the coronavirus but there are many cases where it can be misclassified as some techniques do not provide any goodness if it can only identify type of diseases and ignore the rest. This work is a deep learning-based model to distinguish between cases of COVID-19 from other chest diseases which is need of today.

Results: A Deep Neural Network model provides a significant contribution in terms of detecting COVID-19 and provide effective analysis of chest related diseases with respect to age and gender. Our model achieves 87% accuracy in terms of Gan based synthetic data and four different types of deep learning- based models which provided state of the art comparable results.

Conclusion: If the gap in identifying of all viral pneumonias is not filled with effective automation of chest disease detection the healthcare industry may have to bear unfavorable circumstances.


Deep learning, Coronavirus, X-ray, Chest diseases, ResNet-152, Inception-V3.


Department of Information Technology, College of Computer, Qassim University, Buraydah

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