Medical image processing using neutrosophic ensembles.

Abstract

Medical image processing is a cornerstone of computer-aided diagnosis, where the inherent uncertainty and ambiguity of biomedical data often limit the performance of conventional analytical methods. Within this context, neutrosophic theory has emerged as a robust mathematical framework for handling imprecise and inconsistent information. This review article provides a comprehensive overview of the state of the art in medical image processing using neutrosophic sets, covering its theoretical foundations, key techniques for preprocessing, segmentation, and feature extraction, and its integration with artificial intelligence and deep learning models. The advantages of these approaches over classical fuzzy and probabilistic methods are analyzed, together with current limitations regarding scalability, parameter optimization, and clinical workflow integration. Finally, the paper outlines future research directions, emphasizing the potential of hybrid and explainable neutrosophic models to enhance the accuracy, interpretability, and reliability of next-generation precision medicine systems.