  The world is still overwhelmed by the spread of the COVID-19 virus. With over
250 Million infected cases as of November 2021 and affecting 219 countries and
territories, the world remains in the pandemic period. Detecting COVID-19 using
the deep learning method on CT scan images can play a vital role in assisting
medical professionals and decision authorities in controlling the spread of the
disease and providing essential support for patients. The convolution neural
network is widely used in the field of large-scale image recognition. The
current method of RT-PCR to diagnose COVID-19 is time-consuming and universally
limited. This research aims to propose a deep learning-based approach to
classify COVID-19 pneumonia patients, bacterial pneumonia, viral pneumonia, and
healthy (normal cases). This paper used deep transfer learning to classify the
data via Inception-ResNet-V2 neural network architecture. The proposed model
has been intentionally simplified to reduce the implementation cost so that it
can be easily implemented and used in different geographical areas, especially
rural and developing regions.
