XMorph: Explainable Brain Tumor Analysis Via LLM-Assisted Hybrid Deep Intelligence
2026-02-24 • Computer Vision and Pattern Recognition
Computer Vision and Pattern RecognitionArtificial Intelligence
AI summaryⓘ
The authors developed XMorph, a new AI tool to help identify three types of brain tumors by focusing on the detailed shapes and edges of tumors, which are important in diagnosis. They introduced a method called Information-Weighted Boundary Normalization to highlight important tumor boundaries, making the model more interpretable. The system also uses visual explanations and text descriptions to show how it makes decisions, helping doctors understand the results. Their approach achieves high accuracy while being easier to explain and run efficiently.
Deep learningBrain tumor classificationGliomaMeningiomaPituitary tumorExplainable AIGradCAM++Information-Weighted Boundary NormalizationMorphological featuresMedical imaging
Authors
Sepehr Salem Ghahfarokhi, M. Moein Esfahani, Raj Sunderraman, Vince Calhoun, Mohammed Alser
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.