A survey of the state-of-the-arts on neutrosophic sets in biomedical diagnoses

Abstract

In real world applications, soft computing is an inspirational domain for encoding imprecision and uncertainty. Soft computing procedures integrated with medical applications can support the existing medical systems to allow solutions for unsolvable problems. Fuzzy techniques have extensive solutions for the medical domain applications; however incorporating a new neutrosophic approaches in the medical domain proves its superiority. The current study reported the main neutrosophic sets (NS) definitions along with different medical applications based on NS. In addition, an extensive discussion for the possibility of prolonging the abilities of the fuzzy systems using the neutrosophic systems was included. The preceding studies established that the NS has a significant role in medical images de-noising, clustering, and segmentation. As a future scope, it was suggested that the neutrosophic medical systems can be exploited for neutrosophic scores; continuous truth/indeterminate/falsity versions of conventional score schemes. The integrated methods of the NS in medical domain would lead to tabular or rule-based mapping from input to output variables. The qualitative simulation of the reported studies established that the neutrosophic model based diagnosis is promising aspirants for future research. Furthermore, the current work highlighted the main medical image processes that can be developed using the NS, including de-noising, thresholding, segmentation, clustering and classification. The general algorithms that can be used to include NS in each task were proposed.