Covid19 Infection Detection and Classification Using CNN On Chest X-ray Images

Abstract

Covid-19 has opened up a plethora of worries to the world since the past 2 years. The infection rate and death rate are increasing rapidly. It has worsened by the number of genetic mutations this virus has undergone. Timely detection of the disease is the only way out to handle this health emergency. Severity of this disease is when the virus attacks the major volume of the lung and results in pneumonia. To diagnose the pneumonia the first preferred modality is chest X-ray. There are two solid reasons why the Computer Aided Diagnosis (CAD) system is the need of the hour. First, the volume of X-rays generated for a huge number of infected patients to be assessed and second being the requirement of accuracy in diagnosis. Radiologists find it difficult to assess the severity through bare eyes and most of the time end up making a wrong conclusion which is chaotic decision. With the advent of technology, deep learning algorithms are proving to be most appropriate because of its ability to deliver expected accuracy and capacity to handle huge volume of data. This paper proposed a Deep Learning based Computer Aided Diagnosis System that accepts Chest X-ray image of a patient as input and classifies them as pneumonia or non-pneumonia. The Deep learning model is built and is trained with over 5000 chest X-ray images. Thus, trained model is then tested and validated and an accuracy of 96.66% is achieved. However, since the data is not real time, this work does not claim medical accuracy. The validation plots of the training loss and accuracy and validation loss and accuracy have been validated through regression.

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