Identification and Classification of Pneumonia using CNN Model with Chest X- ray Image

Abstract Pneumonia is a bacterial, fungal, or viral infection that affects one or both lungs. It is a serious disease in which the air sacs in both lungs become clogged with pus and other substances. So, several frameworks and models have been built to properly assess such diseases, but there is still space for improvement. In this study, we used CXR images to train a CNN model to detect and classify Pneumonia disease in the lungs and also present how training accuracy and validation accuracy as well as training loss and validation loss vary when changing the size of the input image. The Kaggle CXR dataset is used, which was already created and pre-processed. The Convolutional Neural Network method is used in the research in close collaboration with some other data augmentation frameworks to enhance classification accuracy, which also will help to enhance training and validation accuracies, as well as characterize the precision of the Convolutional Neural Network model and achieve various results. The training and validation accuracy of our model are 0.9757 and 0.9568, respectively, and the training and validation loss are 0.0857 and 0.1399.