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

TL;DR

This paper presents a hybrid framework combining deep learning and model checking to improve the accuracy and reliability of brain tumor detection and validation in medical imaging, achieving high performance metrics.

Contribution

It introduces a novel integration of model checking with CNN-based feature extraction and clustering for enhanced tumor detection and validation.

Findings

Achieved 98% accuracy in tumor detection

Attained 96.15% precision and 100% recall

Demonstrated robustness and effectiveness of the framework

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.