Classification of Chest X-Ray Images to Detect Pneumonia using CNN and Transfer Learning

Abstract

Pneumonia kills more than 1.4 million children per year. It is among the top diseases which caused many deaths around the world. Because the diagnosis by chest x-ray films is rather complicated, the accuracy of diagnosing pneumonia is not high even by expert radiologists. In this study, the implementation of a deep learning technique for chest x-rays is presented. For this, we have proposed a new method to simplify the process of pneumonia detection. A convolutional neural network (CNN) model “ConvNet-21” has been proposed to extract important features from the chest x-ray images. The dataset is divided into two categories: training and testing, with each category further subdivided into normal and pneumonia. Several data augmentation techniques are applied to the dataset. In this study, five different models have been discussed. There are five convolutional layers in the proposed CNN model and the remaining four are transfer learning models, VGG16, VGG19, Inception-V3, and ResNetl52V2. We have analyzed the performance of all these models by using different performance measure parameters. The accuracy obtained by our proposed CNN model is SS.94%. The accuracy obtained by VGG16, VGG19, Inception-V3 and ResNetl52V2 are 92.7S%, 91.50%, 74.19% and 77.72%, respectively.