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

Elhoucine Elfatimi; Lahcen El Fatimi; Hanifa Bouchaneb

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arXiv · 2501.01991

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

Elhoucine Elfatimi·Lahcen El Fatimi·Hanifa Bouchaneb

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Abstract

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.

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BibTeX RIS

Elhoucine Elfatimi, Lahcen El Fatimi, Hanifa Bouchaneb. 2024-12-31. A Hybrid Deep Learning and Model-Checking Framework for Accurate Brain Tumor Detection and Validation. https://arxiv.org/abs/2501.01991

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