Topological Neutrosophic Analysis for Uncertainty-Aware Thyroid Nodule Classification in Ultrasound Imaging

The classification of thyroid nodules in ultrasound imaging remains clinically challenging due to inherent ambiguities in visual interpretation, signal noise, and overlapping morphological features. To address these limitations, this study introduces an innovative diagnostic framework integrating Neutrosophic Set Theory with topological analysis to quantify and interpret uncertainty in medical image classification. Leveraging a Neutrosophic Neural Network (NNN), image features are mapped into a tripartite representation (truth, indeterminacy, and falsity), enabling granular modeling of diagnostic uncertainty. Further, the framework embeds classification outcomes within a neutrosophic topological space to reveal latent relational patterns such as confidence boundaries, ambiguity propagation, and misclassification topology that conventional metrics overlook.