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

arXiv: https://arxiv.org/abs/2501.01991 · HTML: https://mlsanity.com/paper/2501.01991/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 34 references that are in the map.
- First posted on arXiv 31 Dec 2024.

## Builds on (most cited first, 6 of 6)

- [Deep Learning Ensembles for Melanoma Recognition in Dermoscopy Images](https://mlsanity.com/paper/1610.04662/) (2016)
- [Monitoring Mobile and Spatially Distributed Cyber-Physical Systems](https://mlsanity.com/paper/1904.08847/) (2019)
- [Specifying and Verifying Properties of Space - Extended Version](https://mlsanity.com/paper/1406.6393/) (2014)
- [Qualitative and Quantitative Monitoring of Spatio-Temporal Properties with SSTL](https://mlsanity.com/paper/1706.09334/) (2017)
- [VoxLogicA: a Spatial Model Checker for Declarative Image Analysis (Extended Version)](https://mlsanity.com/paper/1811.05677/) (2018)
- [Spatial Logics and Model Checking for Medical Imaging (Extended Version)](https://mlsanity.com/paper/1811.06065/) (2018)

## Abstract (arXiv)

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.