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Generative Adversarial Synthesis and Deep Feature Discrimination of Brain Tumor MRI Images

Md Sumon Ali, Muzammil Behzad

TL;DR

The paper tackles data scarcity in brain tumor MRI by employing a DC-GAN to generate synthetic 128×128 grayscale MRIs and pairing them with real scans to train a CNN tumor classifier. The approach incorporates progressive training, spectral normalization, and an auxiliary classifier to boost realism and diagnostic usefulness. Results show the CNN achieves approximately 99.7% accuracy on a test set when trained on a merged real+synthetic dataset, indicating that GAN-generated images can meaningfully augment limited medical data. This work demonstrates the practical potential of combining generative and discriminative models for AI-assisted MRI diagnosis, while outlining rigorous validation and ethical considerations for real-world deployment.

Abstract

Compared to traditional methods, Deep Learning (DL) becomes a key technology for computer vision tasks. Synthetic data generation is an interesting use case for DL, especially in the field of medical imaging such as Magnetic Resonance Imaging (MRI). The need for this task since the original MRI data is limited. The generation of realistic medical images is completely difficult and challenging. Generative Adversarial Networks (GANs) are useful for creating synthetic medical images. In this paper, we propose a DL based methodology for creating synthetic MRI data using the Deep Convolutional Generative Adversarial Network (DC-GAN) to address the problem of limited data. We also employ a Convolutional Neural Network (CNN) classifier to classify the brain tumor using synthetic data and real MRI data. CNN is used to evaluate the quality and utility of the synthetic images. The classification result demonstrates comparable performance on real and synthetic images, which validates the effectiveness of GAN-generated images for downstream tasks.

Generative Adversarial Synthesis and Deep Feature Discrimination of Brain Tumor MRI Images

TL;DR

The paper tackles data scarcity in brain tumor MRI by employing a DC-GAN to generate synthetic 128×128 grayscale MRIs and pairing them with real scans to train a CNN tumor classifier. The approach incorporates progressive training, spectral normalization, and an auxiliary classifier to boost realism and diagnostic usefulness. Results show the CNN achieves approximately 99.7% accuracy on a test set when trained on a merged real+synthetic dataset, indicating that GAN-generated images can meaningfully augment limited medical data. This work demonstrates the practical potential of combining generative and discriminative models for AI-assisted MRI diagnosis, while outlining rigorous validation and ethical considerations for real-world deployment.

Abstract

Compared to traditional methods, Deep Learning (DL) becomes a key technology for computer vision tasks. Synthetic data generation is an interesting use case for DL, especially in the field of medical imaging such as Magnetic Resonance Imaging (MRI). The need for this task since the original MRI data is limited. The generation of realistic medical images is completely difficult and challenging. Generative Adversarial Networks (GANs) are useful for creating synthetic medical images. In this paper, we propose a DL based methodology for creating synthetic MRI data using the Deep Convolutional Generative Adversarial Network (DC-GAN) to address the problem of limited data. We also employ a Convolutional Neural Network (CNN) classifier to classify the brain tumor using synthetic data and real MRI data. CNN is used to evaluate the quality and utility of the synthetic images. The classification result demonstrates comparable performance on real and synthetic images, which validates the effectiveness of GAN-generated images for downstream tasks.

Paper Structure

This paper contains 17 sections, 4 equations, 8 figures, 3 tables.

Table of Contents

  1. Introduction
  2. Background and Significance
  3. Challenges in Current Techniques
  4. Problem Statement
  5. Scope of Study
  6. Literature Review
  7. Existing Techniques and Related Work
  8. Limitations in Existing Approaches
  9. Proposed Methodology
  10. Proposed Enhancements
  11. Algorithm and Implementation
  12. Loss Function and Optimization
  13. Experimental Design and Evaluation
  14. Experiment Setup
  15. Performance Metrics
  16. ...and 2 more sections

Figures (8)

  • Figure 1: Overview of the image generation process using noise and real MRI image.
  • Figure 2: Architecture of the DC-GAN model for brain MRI image generation and Classification.
  • Figure 3: Progression of Brain Tumor MRI Image Generation Across Training Epochs.
  • Figure 4: Generated Brain Tumor MRI Images After Final Training Epoch
  • Figure 5: Distribution Comparison Between Real and Generated Brain MRI Images
  • ...and 3 more figures