Neutrosophic-Based Feature Set (Nbfs) For Brain Tumor Detection Using GLCM Features and KNN Classifier on Mri Images

Brain tumor detection using Magnetic Resonance Imaging (MRI) is essential for early diagnosis and treatment planning. However, MRI images often contain noise, uncertainty, and indistinct tumor boundaries, which challenge the effectiveness of traditional feature extraction and classification techniques. Many existing methods fail to handle ambiguous regions robustly and overlapping tissue characteristics, leading to decreased diagnostic reliability. To address these limitations, this study proposes a novel computer-aided diagnosis framework based on Neutrosophic Bipolar Fuzzy Set (NBFS) theory, which integrates neutrosophic logic with bipolar fuzzy reasoning. This approach enables the simultaneous representation of both positive and negative degrees of truth, indeterminacy, and falsity, improving the modeling of complex and uncertain regions in brain MRI scans. The images are transformed into the NBFS domain, and texture features are extracted from the Truth (T), Indeterminacy (I), and Falsity (F) components using the Gray Level Co-occurrence Matrix (GLCM). These features are used both individually and in combination to train a K-Nearest Neighbor (KNN) classifier. Experimental results demonstrate that the NBFS-based framework achieves higher classification accuracy, sensitivity, specificity, and precision compared to conventional texture-based approaches. The confusion matrix analysis further confirms reduced misclassification rates, highlighting the robustness of the method. These findings establish the NBFS framework as a promising tool for improving brain tumor detection in clinical decision support systems, especially under uncertain imaging conditions.