Generative Adversarial Networks (GANs) have emerged as a powerful deep learning framework for generating realistic synthetic data, with significant applications in medical imaging. This study explores the implementation of GAN architectures for medical image generation aimed at improving data availability, model generalization, and diagnostic accuracy in healthcare systems. Medical imaging datasets often suffer from limited availability, class imbalance, and privacy restrictions, making GAN-based data augmentation a viable solution. The proposed approach investigates the performance of various GAN models—such as Deep Convolutional GAN (DCGAN) and Conditional GAN (cGAN)—in producing high-quality synthetic images of modalities including MRI, CT, and X-ray scans. Evaluation metrics such as Structural Similarity Index (SSIM), Peak Signal-to-Noise Ratio (PSNR), and Fréchet Inception Distance (FID) were used to assess image fidelity and diversity. Experimental results demonstrate that GAN-generated images can effectively enhance training datasets and improve the performance of classification models. The findings highlight the potential of GANs in medical image synthesis, supporting clinical decision-making and enabling robust AI-driven diagnostic systems.

In conclusion, the project “Exploring Medical Image Generation Using Generative Adversarial Networks” holds significant promise in revolutionizing medical imaging and diagnostics. By leveraging advanced deep learning techniques, particularly Generative Adversarial Networks (GANs), the project aims to address the challenge of data scarcity in medical image datasets, particularly for kidney X- ray images. Through the generation of synthetic medical images that closely resemble real- world examples, the project facilitates the augmentation of existing datasets, thereby enhancing the training and evaluation of machine learning models for kidney diagnostics. The implementation of the project involves a multi-step process, including data collection, preprocessing, model training, evaluation, and integration with the Streamlit framework for interactive visualization. The integration of GAN-based image generation capabilities into a user-friendly web application enables healthcare professionals and researchers to generate synthetic medical images on-demand, explore different diagnostic scenarios, and evaluate model performance in real-time.

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