| dc.description.abstract | An enormous number of children die due to pneumonia every year worldwide. Pneumonia is a large-scale cause of death amongst children, with a high prevalence rate in South Asia and Sub-Saharan Africa. Even in a developed country like the United States, pneumonia is among the top 10 causes of death. Early detection and treatment of pneumonia can significantly scale down fatality rates among children in countries with a high prevalence. Hence, this paper presents a Convolutional Neural Network model to detect pneumonia using x-ray images. A ResNet50 and VGG16 pre-trained model was trained to classify x-ray images into two classes, viz., pneumonia and non-pneumonia, by changing various parameters, hyperparameters, and the number of convolutional layers. The VGG-16 model has better performance than ResNet50. Meanwhile, ResNet50 achieved the testing time faster than VGG-16. The CNN model had a better performance in classifying pneumonia and non-pneumonia images | en_US |
