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Enhancing the Synthetic Medical Images in Healthcare Using AI-based Exposed GANs with Data Augmentation.

Rupali Atul Mahajan1, Mudassir Khan2, Rajesh Dey3

  • 1Vishwakarma Institute of Technology, Computer Science and Engineering, Pune, India.

Current Medical Imaging
|December 2, 2025
PubMed
Summary

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This summary is machine-generated.

This study explores using Generative Adversarial Networks (GANs) to create synthetic medical images, aiming to improve healthcare AI accuracy. The research demonstrates GANs

Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Computer Vision

Background:

  • Healthcare AI accuracy is crucial for reliable diagnostics.
  • Generating realistic synthetic medical images is a key challenge.
  • Existing methods may produce images not reflective of real data, impacting AI performance.

Purpose of the Study:

  • To evaluate the efficacy of Generative Adversarial Networks (GANs) in producing synthetic medical images.
  • To enhance the accuracy of healthcare AI systems through improved synthetic data generation.
  • To investigate the Exposed GAN architecture for realistic medical image synthesis.

Main Methods:

  • Utilized the Medical Segmentation Decathlon (MSD) dataset for training.
  • Employed data pre-processing, including pixel value normalization.
Keywords:
Artificial intelligence (AI)Generative adversarial networks (GAN)HealthcareImage generationMedical segmentation decathlon (MSD).Synthetic medical

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  • Implemented the Exposed GAN architecture with adversarial training and data augmentation techniques.

    • Main Results:

    • The discriminator achieved an accuracy of 0.6924 on real data and 0.78789 on fake data.
    • Achieved an average accuracy rate (MPa) of 96.29%, indicating successful synthetic image generation.
    • Demonstrated the potential of GANs to generate realistic synthetic medical images.

    Conclusions:

    • Exposed GANs show promise for generating high-quality synthetic medical images.
    • Synthetic medical images generated by GANs can potentially improve healthcare AI diagnostic accuracy.
    • Further research into unifying synthetic medical image generation techniques is warranted for broader AI applications.