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

TL;DR

XMorph is an explainable, efficient AI framework for precise brain tumor classification that combines visual and textual interpretability, achieving high accuracy and aiding clinical decision-making.

Contribution

The paper introduces XMorph, a novel hybrid deep learning framework that integrates boundary-focused normalization and dual-channel explainability with LLM-generated rationales for brain tumor analysis.

Findings

Achieves 96.0% classification accuracy.

Provides clinically interpretable visual and textual explanations.

Demonstrates that explainability can be combined with high performance.

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.