Neuroflux: An Interpretable Multimodal U-Net Model for Glioblastoma Multiforme Segmentation Through Grad-CAM

Glioblastoma multiforme (GBM) is one of the most aggressive malignant brain tumors, with early intervention through diagnosis and treatment being critical to improving the low median overall survival rate of 15 months.Manual anatomical segmentation is problematic because it is prone to inconsistencies from interobserver variation.Current models are highly accurate but lack computational efficiency and multimodality, which limits the clinical applicability of such segmentation tools in hospital environments.Neuroflux is a multimodal U-Net-based deep learning model for GBM segmentation using the BraTS 2020 dataset.Adapted from a baseline model, we enhanced the preprocessing of raw data through MinMax scaling and applied our custom hybrid loss strategy to maintain high levels of accuracy while minimizing false negatives.With further improvements in regularization techniques, Neuroflux was also fine-tuned to incorporate targeted modifications and optimized dropout rates, which improved the training strategy.Overall, an accuracy of 99.01%, precision of 99.11%, sensitivity of 98.85%, and specificity of 99.7% was achieved while significantly improving clinician interpretability.Our high-precision and efficient modified architecture highlights the potential for new lightweight and interpretable models to enhance diagnostic reliability and treatment planning.