Enhancing Skin Cancer Diagnosis using Cubic Pythagorean Fuzzy Hypersoft Set with Salp Swarm Algorithm.

Selmi, Afef; Issaouı, Imène

Due to the rapid increase in population density, medical sciences now face a major challenge in the automated detection of diseases. Intelligent system assists health personnel in earlier disease diagnosis and provides reliable treatment to reduce the fatality rates. Skin cancer is one of the most severe and deadliest kinds of cancer. A health professional uses dermoscopic images to manually diagnose skin tumors. This technique can be time-consuming and labor-intensive and needs a considerable level of expertise. The automatic recognition method is essential for the earlier diagnosis of skin tumors. In recent times, N-soft Set model has become widespread, which is a generalization of fuzzy set where all the elements have a membership value in the complement (0 to 1) and in the set (0 or 1). This study presents a Skin Cancer Diagnosis using Cubic Pythagorean Fuzzy Hypersoft Set (SCD-CPFHSS) technique. The presented SCD-CPFHSS technique performs identification of skin cancer using the application of NSs and metaheuristic algorithms. In the SCD-CPFHSS technique, neural architectural search network (NASNet) model derives feature extractors from the dermoscopic image. In addition, the efficacy of the NASNet model can be boosted by the design of salp swarm algorithm (SSA). For skin cancer recognition, the SCD-CPFHSS technique applies CPFHSS model. The experimental outcome of the SCD-CPFHSS methodology was validated using medical dataset. The extensive results pointed out that the SCD-CPFHSS technique reaches better results on skin cancer diagnosis

SKIN cancer diagnosis; PYTHAGOREAN theorem; NEUTROSOPHIC logic; ANT algorithms; FUZZY systems

International Journal of Neutrosophic Science (IJNS), 2025, Vol 25, Issue 1, p148

2692-6148

Academic Journal

10.54216/IJNS.250113