Potential Role of Generative Adversarial Networks in Enhancing Brain Tumors.

Area of Science:

• Artificial Intelligence
• Medical Imaging
• Neuro-oncology

Background:

• Contrast enhancement is crucial for brain tumor diagnosis and treatment planning.
• Current methods can be invasive or time-consuming.
• Developing AI-driven solutions offers a novel approach to medical imaging enhancement.

Purpose of the Study:

• To investigate the efficacy of generative adversarial neural networks (GANs) in creating artificial contrast enhancement for brain tumors.
• To evaluate a lightweight AI model for generating tumor contrast enhancement.
• To assess the potential of AI in simplifying and improving brain tumor visualization.

Main Methods:

• Retrospective analysis of 156 MRI scans from 129 brain tumor patients (2020-2023).
• Development and training of a GAN to mimic real contrast enhancement.
• Quantitative evaluation using VGG-16, ResNet, and image similarity metrics (MAE, MSE, SSIM).
• Qualitative assessment via a satisfaction survey of 23 medical professionals.

Main Results:

• The GAN model achieved VGG loss values of 2,049.8 (training), 2,632.6 (validation), and 4,276.9 (test).
• Structural Similarity Index Measure (SSIM) scores were 0.366 (training), 0.356 (validation), and 0.3192 (test).
• Medical professionals reported a median overall satisfaction score of 7/10 for the AI-generated enhancements.

Conclusions:

• Lightweight generative adversarial neural networks show promise for artificial contrast enhancement in medical imaging.
• The developed AI model could pave the way for more accessible AI-driven imaging solutions.
• Further research is required to achieve clinical applicability and widespread adoption.