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
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.