Design and Development of Generative Adversarial Networks (GANs) for Improved Object Recognition and Synthesis in Computer Vision

Abstract

Generative Adversarial Networks (GANs) are a vital tool in computer vision, offering novel methods for object recognition and image synthesis. Like a game, GANs compete with one another to produce ever-more-realistic images. This work investigates how object recognition can be advanced by utilizing GANs' capacity to provide realistic and varied visual data. By enhancing feature extraction and domain adaption through extensive dataset training, GANs can raise the accuracy of object recognition systems. The suggested approach maximizes the performance of GANs for object identification tasks by utilizing an efficient training technique. The proposed method employs GAN architecture to extract Region of Interest (ROI) from CXR (chest X-ray) pictures.