Applications of Nature-Inspired Metaheuristic Algorithms for Disease diagnosis in medical imaging

Abstract

Nature-Inspired Metaheuristics Algorithms (NIMHs) have also been given attention in recent years in the field of medical imaging to diagnose diseases because they can efficiently deal with complex optimization problems. These Algorithms are motivated by natural phenomena and natural evolution. This survey emphasizes recent progress in using algorithms like particle swarm optimization (PSO), grey wolf optimizer (GWO), whale optimization algorithm (WOA), Harris Hawks Optimization (HHO), Salp Swarm Optimization (SSO)and hybrid metaheuristics towards disease diagnosis across image modalities, including MRI, CT and X-ray. When they are merged with contemporary architecture, e.g., U-net, vision transformer (ViT), the techniques enhance diagnostic accuracy and efficiency to identify diseases like COVID-19 classification, tumours and Parkinson's disease. The current review integrates recent developments in the area, yet also emphasizes persisting challenges including increased computational cost, poor generalizability, and absence of interpretability. And the directions for the future are elaborated, including developing effective hybrid models, explainable AI, multi-objective optimization, and clinically validated frameworks on various datasets.