Paper ID: 2501.01991

A Hybrid Deep Learning and Model-Checking Framework for Accurate Brain Tumor Detection and Validation

Lahcen El Fatimi, Elhoucine Elfatimi, Hanifa Bouchaneb

Model checking, a formal verification technique, ensures systems meet predefined requirements, playing a crucial role in minimizing errors and enhancing quality during development. This paper introduces a novel hybrid framework integrating model checking with deep learning for brain tumor detection and validation in medical imaging. By combining model-checking principles with CNN-based feature extraction and K-FCM clustering for segmentation, the proposed approach enhances the reliability of tumor detection and segmentation. Experimental results highlight the framework's effectiveness, achieving 98\% accuracy, 96.15\% precision, and 100\% recall, demonstrating its potential as a robust tool for advanced medical image analysis.

Submitted: Dec 31, 2024