On Performance Analysis of a Neutrosophic Bipolar Fuzzy and Texture-Based Model for COVID-19 Detection from Chest X-Rays

This study proposes a novel hybrid framework for rapid and accurate COVID-19 detection from chest X-ray images by integrating Neutrosophic Bipolar Fuzzy Sets (NBFS) with texture-based feature anal ysis. The methodology comprises four main stages: image acquisition, preprocessing, feature extraction, and classification. In the preprocessing phase, NBFS enhances image contrast by decomposing each image into positive, negative, and indeterminate components using intensity thresholds. The neutrosophic domain assigns truth, falsity, and indeterminacy values, enabling robust handling of noise and uncertainty in medical imaging. Texture features are extracted using the Gray-Level Co-occurrence Matrix (GLCM), capturing spatial inten sity relationships critical for distinguishing infected regions. These features are classified using Decision Tree, Logistic Regression, and k-Nearest Neighbour (k-NN) models. Experimental evaluations on a benchmark chest X-ray dataset demonstrate that the Decision Tree classifier outperformed other models, achieving an accuracy of 95.92%, sensitivity of 96.55%, and specificity of 95.30%. The proposed approach offers a fast, interpretable, and cost-effective diagnostic aid, potentially supporting large-scale screening in clinical settings. Future research will explore multi-class classification and application to diverse medical imaging modalities.