A Hybrid Deep Learning and Model-Checking Framework for Accurate Brain Tumor Detection and Validation
This addresses the problem of reliable tumor detection for medical imaging, but it is incremental as it combines existing techniques.
The paper tackled brain tumor detection and validation in medical imaging by integrating model checking with deep learning, achieving 98% accuracy, 96.15% precision, and 100% recall.
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.