XMorph: Explainable Brain Tumor Analysis Via LLM-Assisted Hybrid Deep Intelligence

TL;DR

An Information-Weighted Boundary Normalization (IWBN) mechanism is proposed that emphasizes diagnostically relevant boundary regions alongside nonlinear chaotic and clinically validated features, enabling a richer morphological representation of tumor growth.

Abstract

Deep learning has significantly advanced automated brain tumor diagnosis, yet clinical adoption remains limited by interpretability and computational constraints. Conventional models often act as opaque ''black boxes'' and fail to quantify the complex, irregular tumor boundaries that characterize malignant growth. To address these challenges, we present XMorph, an explainable and computationally efficient framework for fine-grained classification of three prominent brain tumor types: glioma, meningioma, and pituitary tumors. We propose an Information-Weighted Boundary Normalization (IWBN) mechanism that emphasizes diagnostically relevant boundary regions alongside nonlinear chaotic and clinically validated features, enabling a richer morphological representation of tumor growth. A dual-channel explainable AI module combines GradCAM++ visual cues with LLM-generated textual rationales, translating model reasoning into clinically interpretable insights. The proposed framework achieves a classification accuracy of 96.0%, demonstrating that explainability and high performance can co-exist in AI-based medical imaging systems. The source code and materials for XMorph are all publicly available at: https://github.com/ALSER-Lab/XMorph.

Figures (7)

• Figure 1: The end-to-end pipeline of the proposed XMorph framework for explainable brain tumor classification, illustrating the six-stage workflow. Stage 1 (Automated Tumor Segmentation): An input MRI is processed by a DeepLabV3 model to generate a tumor mask and extract the ROI (tumor boundary). Stage 2 (Tumor Specific Features Extraction): A multi-faceted feature set is extracted. The tumor boundary is converted to a time series to compute nonlinear chaotic descriptors (e.g., Entropies, Fractal Dimension), features from our novel Information-Weighted Boundary Normalization (IWBN), and quantitative clinical biomarkers. Stage 3 (Deep Features Extraction): In a parallel stream, a pre-trained ResNet-50 extracts high-level textural features from the input MRI. Stage 4 (Features Fusion): The Tumor Specific and deep feature sets are concatenated into a single feature vector. Stage 5 (Classification): An XGBoost classifier uses the fused vector to predict the tumor class (Glioma, Meningioma, or Pituitary). Stage 6 (Dual-Channel Explainability): The framework provides two complementary explanations for transparency. A visual GradCAM++ heatmap, generated from the deep feature model, shows where the model focuses. A textual rationale from an LLM explains why the final XGBoost classifier made its decision by interpreting the most influential features from the fused set, including the novel chaotic and clinical descriptors.
• Figure 2: Example of brain tumor segmentation results showing original MRI, masks, and differences.
• Figure 3: The boundary-to-signal pipeline reveals distinct morphological signatures across different tumor types. The pipeline converts 2D tumor boundaries (Column 2) into 1D normalized time-series signals (Column 5). Each row illustrates a representative clinical case, demonstrating the direct relationship between visual morphology and the resulting signal characteristics. (a) Pituitary Tumor: A visually regular, well-circumscribed boundary translates into a smooth, low-amplitude signal, quantitatively confirmed by a low Irregularity Index (STD = 0.142). (b) Meningioma: A characteristic lobulated boundary produces a signal with more pronounced variations, reflecting an intermediate structural complexity (Irregularity Index = 0.157). (c) Glioma: A highly infiltrative and poorly defined malignant boundary generates a chaotic, non-periodic signal with sharp, high-amplitude oscillations, corresponding to a significantly higher Irregularity Index (STD = 0.253). This clear monotonic progression from a smooth to a chaotic signal provides strong visual evidence that the pipeline robustly captures clinically relevant morphological differences.
• Figure 4: Visual Validation of IWBN across Tumor Types. This figure illustrates how our novel IWBN method identifies and emphasizes regions of high morphological complexity. Each column represents a different tumor class, with the top row showing the final Information Weights and the bottom row showing the underlying Local Entropy that generated them. Top Row (Information Weights): Visualizes the amplification factor applied to each point on the boundary. Hotter colors (red/yellow) indicate higher weights, signifying regions of greater diagnostic interest. Bottom Row (Local Entropy): Maps the intrinsic complexity of the boundary. Brighter colors (yellow) correspond to segments with higher irregularity and unpredictability. Pituitary: The benign, regular boundary exhibits uniformly low entropy, resulting in low and evenly distributed weights. The method correctly identifies this as a simple structure. Meningioma: The method accurately pinpoints the lobulated portion of the boundary as a localized region of high entropy, assigning it a correspondingly high weight. Glioma: The malignant, infiltrative boundary shows widespread, high-magnitude entropy. The IWBN method correctly assigns high weights to these numerous chaotic segments, effectively highlighting the features of malignancy.
• Figure 5: Visualization of three key radiological biomarkers across tumor types: (Left) Ring Enhancement Index (core vs. rim intensity), (Center) Midline Shift (centroid deviation from the anatomical midline), and (Right) Skull-to-Tumor Distance (minimum distance and contact ratio). The figure demonstrates increasing enhancement irregularity and deformation gradient from pituitary $\rightarrow$ meningioma $\rightarrow$ glioma, reflecting both vascular and spatial indicators of malignancy.