Improved Classification for Pneumonia Detection using Transfer Learning with GAN based Synthetic Image Augmentation

Deep learning techniques have found their applications in various domains, and they are being widely used in medical treatments and diagnostics. To diagnose diseases viz. pneumonia, the examination of chest X-ray images are often conducted, and the efficiency of diagnosis can be significantly improved with the use of computer-aided diagnostic systems. Deep learning algorithms are used in this paper for the classification of chest X-ray images to diagnose pneumonia. Deep convolutional generative adversarial networks were trained for augmentation of synthetic images to oversample the dataset for the model to perform better. Then transfer learning was used with convolutional neural networks by utilising VGG16 as the base model for image classification. The model was able to achieve 94.5% accuracy on the validation set. In comparison with the naïve models, the accuracy of the proposed model was found to be significantly higher.