用形式化验证提升深度学习脑肿瘤检测的可靠性
A Hybrid Deep Learning and Model-Checking Framework for Accurate Brain Tumor Detection and Validation
- 融合模型检查与CNN、K-FCM实现精准分割
- 准确率98%、召回率100%、精确率96.15%
- 适合医学影像分析与算法可信性研究
模型检查是一种形式化验证技术，可确保系统满足预设要求，在开发过程中有效降低错误并提升质量。本文提出一种新型混合框架，将模型检查与深度学习结合，用于医学影像中的脑肿瘤检测与验证。通过整合模型检查原理、基于CNN的特征提取以及K-FCM聚类进行分割，该方法提升了肿瘤检测与分割的可靠性。实验结果表明，该框架在准确率、精确率和召回率方面表现优异，分别达到98%、96.15%和100%，展现出作为先进医学图像分析工具的巨大潜力。
原文摘要 · Abstract (English)
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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