Synthesizing Realistic Knee MRI Images: A VAE-GAN Approach for Enhanced Medical Data Augmentation

This study presents a novel approach for synthesizing knee MRI images by combining Variational Autoencoders (VAEs) and Generative Adversarial Networks (GANs). By leveraging the strengths of VAEs for efficient latent space representation and GANs for their advanced image generation capabilities, we introduce a VAE-GAN hybrid model tailored specifically for medical imaging applications. This technique not only improves the realism of synthesized knee MRI images but also enriches training datasets, ultimately improving the outcome of machine learning models. We demonstrate significant improvements in synthetic image quality through a carefully designed architecture, which includes custom loss functions that strike a balance between reconstruction accuracy and generative quality. These improvements are validated using quantitative metrics, achieving a Mean Squared Error (MSE) of 0.0914 and a Fréchet Inception Distance (FID) of 1.4873. This work lays the groundwork for novel research guidelines in biomedical image study, providing a scalable solution to overcome dataset limitations while maintaining privacy standards, and pavement of reliable diagnostic tools.

@article{A2024,
  title     = {Synthesizing Realistic Knee MRI Images: A VAE-GAN Approach for Enhanced Medical Data Augmentation},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {15},
  number    = {11},
  year      = {2024},
  publisher = {The Science and Information Organization},
  author    = {Revathi S A and B Sathish Babu},
  doi       = {10.14569/IJACSA.2024.0151178},
  url       = {https://doi.org/10.14569/IJACSA.2024.0151178}
}