A deep learning approach for pneumonia classification from chest X-Ray images with ensemble modelling and explainable AI

Abstract

Pneumonia is one of those frightening diseases that has a high mortality rate among children and the elderly, with an estimated 2 million fatalities per year. Pneumonia affects the poorest people in Africa and Asia the most, due to a lack of medical surveillance in such areas. It is responsible for 28 percent of all child fatalities in Bangladesh each year, and the number is likely to be considerably higher. In recent years, a number of computer-assisted diagnostic methods have been developed to assist in the detection of pneumonia. In this study, an efficient model PNEXAI is proposed to identify pneumonia utilizing Chest X-Ray images. We gathered and classified data using VGG16, VGG19, ResNet 50, ResNet 101 and Inception v3. The accuracy rate of 97.17% was reached by VGG16, 97.69% by VGG19, 97.35%by ResNet50, 95.63% by ResNet101, and 94.86% by Inception V3, respectively. We then developed an ensemble model containing the top three classifications (VGG16, VGG19 and ResNet50) which delivered 98.46 % of best overall accuracy. Finally, to better comprehend our categorization, we included explainable artificial intelligence in our model.