Research shows that the development of artificial intelligence technology brings with it many new opportunities and challenges. In recent years, generative models such as Generative Adversarial Networks (GANs) have become increasingly important for generating realistic images, sounds and text. In this review article, authors propose new GAN architecture enhanced with CLAHE algorithm to improve process of generating grayscale images. We employ CLAHE to preprocess the X-ray images, which enhances the local contrast and highlights the important features necessary for accurate diagnosis. The preprocessed images are then fed into a GAN architecture designed for image analysis classification. We evaluate our approach on a medical dataset of chest X-ray images and achieve a remarkable accuracy of 93.61%, significantly outperforming proposed method. The results demonstrate that the integration of CLAHE with GANs can effectively enhance the diagnostic performance of deep learning models in grayscale imaging.