A Hybrid Deep Learning and Model-Checking Framework for Accurate Brain Tumor Detection and Validation
Fuente:
arXiv
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866908343537238016 |
|---|---|
| author | Elfatimi, Elhoucine Fatimi, Lahcen El Bouchaneb, Hanifa |
| author_facet | Elfatimi, Elhoucine Fatimi, Lahcen El Bouchaneb, Hanifa |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_01991 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A Hybrid Deep Learning and Model-Checking Framework for Accurate Brain Tumor Detection and Validation Elfatimi, Elhoucine Fatimi, Lahcen El Bouchaneb, Hanifa Computer Vision and Pattern Recognition Artificial Intelligence I.2.6; I.4.6 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. |
| title | A Hybrid Deep Learning and Model-Checking Framework for Accurate Brain Tumor Detection and Validation |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence I.2.6; I.4.6 |
| url | https://arxiv.org/abs/2501.01991 |