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COVID-19 was posed as a very infectious and deadly pneumonia type disease until recent times. Novel coronavirus or SARS-COV-2 strain is liable for COVID-19, and it's already shown the destructive nature of respiratory illness by threatening the health of many lives across the world. Clinical study reveals that a COVID-19 infected person may experience a dry cough, muscle pain, headache, fever, sore throat, and mild to moderate respiratory disease. At an equivalent time, it affects the lungs badly with the viral infection. So, the lung is often a prominent viscus to diagnose the gravity of COVID-19 disease using X-Ray and CT scan images of the chest. Despite having lengthy testing time, RT-PCR may be a proven testing methodology to detect coronavirus infection. Sometimes, it'd give false positive and false negative results than the specified rates. Therefore, to help the RT-PCR standard method for accurate clinical diagnosis, COVID-19 screening is often adopted with X-Ray or CT scan images of an individual's lung. During this project, we propose a unique system with a convolutional neural network (CNN) based multi-image augmentation technique for detecting COVID-19 in the chest using X-Ray or chest CT scan images of coronavirus suspected individuals. Thus, our proposed system provides a promising result in a short period.