A Hybrid Deep Learning and Model-Checking Framework for Accurate Brain Tumor Detection and Validation
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.
Code (0)
등록된 구현이 없습니다.
Tasks
Medical Image AnalysisSegmentationSimilar Papers 제목 키워드 기반
Hybrid Multihead Attentive Unet-3D for Brain Tumor Segmentation
Brain tumor segmentation is a critical task in medical image analysis, aiding in the diagnosis and treatment planning of brain tumor patients. The importance of automated and accurate brain tumor segmentation cannot be o…
Brain Tumor SegmentationMedical Image AnalysisSegmentationTumor SegmentationSpatio-spectral classification of hyperspectral images for brain cancer detection during surgical operations
Surgery for brain cancer is a major problem in neurosurgery. The diffuse infiltration into the surrounding normal brain by these tumors makes their accurate identification by the naked eye difficult. Since surgery is the…
Classification Of Hyperspectral ImagesMedical DiagnosisHybrid Model using Feature Extraction and Non-linear SVM for Brain Tumor Classification
It is essential to classify brain tumors from magnetic resonance imaging (MRI) accurately for better and timely treatment of the patients. In this paper, we propose a hybrid model, using VGG along with Nonlinear-SVM (Sof…
Binary ClassificationBrain Tumor ClassificationIntegrating Edges into U-Net Models with Explainable Activation Maps for Brain Tumor Segmentation using MR Images
Manual delineation of tumor regions from magnetic resonance (MR) images is time-consuming, requires an expert, and is prone to human error. In recent years, deep learning models have been the go-to approach for the segme…
Brain Tumor SegmentationSemantic SegmentationTumor SegmentationVGDM: Vision-Guided Diffusion Model for Brain Tumor Detection and Segmentation
Accurate detection and segmentation of brain tumors from magnetic resonance imaging (MRI) are essential for diagnosis, treatment planning, and clinical monitoring. While convolutional architectures such as U-Net have lon…
Medical Image SegmentationTumor Segmentation