A Review on Metaheuristic Algorithms with Neutrosophic Sets for Image Enhancement

Abstract

Breast cancer has emerged as a major killer in recent years. With a yearly rate of about one million new cases, it is the most prevalent among women in the world's poorest countries. Grading of cellular images has emerged as a key prognostic factor during the past decade. Neutrosophic sets used to enhance medical images in the last decade. Neutrosophic sets can overcome the uncertainty and indeterminacy of information. In recent years, metaheuristics have integrated with neutrosophic sets. Because of their adaptability, simplicity, and task independence, metaheuristics have been extensively employed to tackle many difficult non-linear optimization problems. The purpose of this research is to investigate several approaches to image classification for breast cancer pictures. This includes the use of metaheuristics and neutrosophic sets for optimization and image enhancement. This research was undertaken to better understand the current state of the art in breast cancer identification from medical pictures and to provide insight into the difficulties that lie ahead. We hope that this will encourage academics to investigate hitherto understudied facets of breast cancer identification.