Medical Image Synthesis Using DCGAN for Chest X-Ray Images

Medical reconstruction of images is essential for deep learning model training, improving diagnosis efficiency, and augmenting small datasets for a range medical purpose. In this work, we investigate the synthesis of realistic and superior chest X-ray pictures using Deep Convolutional neural networks Generative Adversarial Networks (DCGAN). A vital diagnosing technique for breathing problems disorders is chest X-ray imaging, and the development and assessment of AI-based medical devices can benefit from a greater amount of numerous, artificially produced information. We offer a novel method that creates synthetic chest Xray pictures that closely replicate real radiography data by utilizing DCGAN, a generative model architecture. Our solution is based on a two-step process: a discriminator network and a generator. The discriminator discerns among real and fake pictures, while a generator trains to produce accurate images of the chest. Through an intensive training regimen, these two networks continuously enhance the standard of the pictures they produce. We ran comprehensive tests on a variety of chest X-ray image datasets to confirm the efficacy of our methodology. Our DCGAN model produced artificial pictures that bore striking visual similarities to actual X-ray images of the chest, including radiographic noise, disease patterns, and anatomical features. In order to statistically evaluate the diversity and realism of the created photos, we also used a variety of assessment measures, which we used to show that our model created extremely convincing synthetic data. The synthesised chest X-ray pictures might be useful assets for expanding on small datasets, which will help build strong and precise deep-learning algorithms for the detection and classifications of pulmonary diseases. These pictures may also be utilized to improve computer-aided diagnosis system training and validation, which will help medical personnel make better judgements and provide better treatment for patients.