XMorph achieves 96% accuracy in classifying glioma, meningioma, and pituitary tumors
The framework uses Information-Weighted Boundary Normalization to capture complex tumor boundaries
Dual-channel explainable AI combines visual cues with textual rationales
The system addresses the 'black box' problem in medical AI
Source code is publicly available for further research
📖 Full Retelling
Researchers led by Sepehr Salem Ghahfarokhi and including M. Moein Esfahani, Raj Sunderraman, Vince Calhoun, and Mohammed Alser introduced XMorph, an explainable AI framework for brain tumor analysis, in a paper submitted to arXiv on February 24, 2026, to address the interpretability and computational limitations of conventional deep learning models in medical imaging. The system represents a significant advancement in automated brain tumor diagnosis by providing both high accuracy and explainability, which has been a major challenge in the field. XMorph is specifically designed to classify three prominent brain tumor types: glioma, meningioma, and pituitary tumors with remarkable 96% accuracy. The innovation behind XMorph lies in its Information-Weighted Boundary Normalization mechanism that emphasizes diagnostically relevant boundary regions alongside nonlinear chaotic and clinically validated features, enabling a more comprehensive morphological representation of tumor growth. This approach addresses a critical limitation of conventional models that often fail to quantify the complex, irregular tumor boundaries characteristic of malignant growth. The framework's dual-channel explainable AI module combines GradCAM++ visual cues with LLM-generated textual rationales, translating complex model reasoning into clinically interpretable insights that can be understood by medical professionals.
🏷️ Themes
Medical AI, Explainable AI, Brain tumor diagnosis, Computational efficiency
Technique and process of creating visual representations of the interior of a body
Medical imaging is the technique and process of imaging the interior of a body for clinical analysis and medical intervention, as well as visual representation of the function of some organs or tissues (physiology). Medical imaging seeks to reveal internal structures hidden by the skin and bones, as...
In machine learning, deep learning focuses on utilizing multilayered neural networks to perform tasks such as classification, regression, and representation learning. The field takes inspiration from biological neuroscience and revolves around stacking artificial neurons into layers and "training" t...
Within artificial intelligence (AI), explainable AI (XAI), generally overlapping with interpretable AI or explainable machine learning (XML), is a field of research that explores methods that provide humans with the ability of intellectual oversight over AI algorithms. The main focus is on the reaso...
--> Computer Science > Computer Vision and Pattern Recognition arXiv:2602.21178 [Submitted on 24 Feb 2026] Title: XMorph: Explainable Brain Tumor Analysis Via LLM-Assisted Hybrid Deep Intelligence Authors: Sepehr Salem Ghahfarokhi , M. Moein Esfahani , Raj Sunderraman , Vince Calhoun , Mohammed Alser View a PDF of the paper titled XMorph: Explainable Brain Tumor Analysis Via LLM-Assisted Hybrid Deep Intelligence, by Sepehr Salem Ghahfarokhi and 4 other authors View PDF HTML 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 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: this https URL . Comments: Accepted in ICCABS 2026: The 14th International Conference on Computational Advances in Bio and Medical Sciences Subjects: Computer Vision and Pattern Recognition (cs.CV) ; Artificial Intelligence (cs.AI) Cite as: arXiv:2602.21178 [cs.CV] (or arXiv:2602.21178v1 [cs.CV] for this ve...
