A Comprehensive Framework Analysis of Cycle GAN-Based Modality Translation: Enhancing Brain Tumor Diagnostics from FLAIR to T2w

Brain tumors remain a major healthcare challenge due to their complexity, high mortality rates, and profound impact on patients' lives, making accurate diagnosis and treatment crucial. Multi-modal MRI scans, particularly FLAIR and T2-weighted (T2w) images, offer complementary information about tumor structure and progression. However, real-world clinical settings often face the challenge of missing imaging modalities, limiting comprehensive assessments. To address this issue, we propose a CycleGAN-based framework for translating between FLAIR and T2w MRI scans using the BraTS dataset from the Medical Decathlon Challenge. This dataset includes 3D MRI scans with segmentation masks outlining key tumor regions such as edema, non-enhancing tumor, and enhancing tumor. Our framework uses U-Net-based generators and PatchGAN discriminators, optimized with multiple loss functions, including adversarial, cycle consistency, structural similarity index (SSIM), and pixel-wise losses. These ensure that the generated images are both anatomically accurate and visually realistic. We apply preprocessing steps like intensity normalization, background removal, and data augmentation to maintain structural details and enhance training stability. Our quantitative evaluation shows promising results, achieving SSIM scores of 0.8226 for T2w and 0.7767 for FLAIR. Qualitative analysis further highlights improved tumor visibility and clearer anatomical structures, particularly around tumor boundaries. By addressing the challenge of incomplete imaging datasets, our method not only enhances data availability for tasks like tumor segmentation but also supports more comprehensive diagnostic workflows. This approach represents a step forward in advancing precision medicine for brain tumor analysis through multi-modal MRI synthesis.