Multi-class Classification for the Identification of COVID-19 in X-Ray Images Using Customized Efficient Neural Network

During the global urgency, experts from all over the world searching for a new technology that supports the COVID19 pandemic. The deep learning and artificial intelligence application used the researchers on the previous epidemic, which encouraged a new angle to fight against the COVID19 outbreak. The limited number of COVID19 kits available in hospitals is due to the increasingly high number of cases. Therefore, it is necessary to implement an alternative system that detects and diagnoses the COVID19 and stops spreading among people. This chapter aims to detect and classify COVID19 infected, normal, and pneumonia patients from X-ray images using deep learning techniques (proposed CNN, AlexNet, and VGG16 models). The experiment was performed by combining two datasets, which are available on the Kaggle repository. The result analysis shows that the proposed CNN model achieved the highest accuracy of 95% from other deep learning models (AlexNet 90% of accuracy, and VGG16 94% of accuracy).