Affiliation:
1. National Institute of Technology, Warangal, India
2. National Institute of Technology, Raipur, India
3. Visvesvaraya National Institute of Technology, Nagpur, India
Abstract
This difficult era of SARS COVID, a deadly viral disease, which first got spread in Wuhan, a city in China and after that to the whole nation, has become a topic of great concern due to less efficiency in the detection tools which are used in hospitals. In this chapter, the authors have used deep learning frameworks to detect Covid-19, pneumonia, tuberculosis, and no-findings using chest x-ray images. Till this time, no work has been done for classifying the above four classes using a single deep learning model. As this study contains four classes which all are quite similar, so the authors tried various neural network architectures which can deeply analyze to separate the features. The authors have used pre-trained DenseNet-121 for classification purpose and have extended some dropout layers and normalization layers at last to reduce overfitting. As a result of this experiment, the authors achieved a validation accuracy of 97.38%. In this study they have tried to differentiate between four different classes.