Enhancing Pneumonia Detection in Chest X-Rays: A Combined GAN and CNN Approach

Generative adversarial networks (GANs) are powerful generative models that have lead to advances in image generation and medical image synthesis tasks. GAN consists of two neural network models such as Generator and Discriminator, which are trained against each other. Medical image synthesis has gained significant attention in recent years, driven by the need for diverse and representative datasets for training machine learning models in the field of medical image analysis. Convolutional Neural Network (CNN) is a type of Artificial Neural Network, which is organised into the layers of neurons that are responsible for detecting different features in an image. This work focuses on the application of GANs to synthesize pulmonary chest X-ray images (a crucial diagnostic tool in pulmonary medicine) and to train a CNN model for Pneumonia detection. Hence, the proposed model leverages the power of deep learning to generate synthetic X-ray images that closely resemble real patient data so that the performance of diagnosis can be improved. Overall, this work contributes to the advancement of medical image synthesis techniques, facilitating the development of more accurate and reliable tools for diagnosing pulmonary diseases and improving patient care.