Advanced Meta-Heuristic Algorithm Based on AlBiruni Earth Radius and Particle Swarm Optimization Methods for Oral Cancer Detection

Oral cancer is a widespread and potentially fatal disease, often diagnosed in later stages, leading to poor outcomes and increased mortality rates. Early detection is crucial for improving patient prognosis and survival rates. To address this challenge, a novel method for early oral cancer diagnosis is proposed in this study, utilizing advanced meta-heuristic algorithms such as Particle Swarm Optimization (PSO) and the Al Biruni Earth Radius Algorithm (ABERA). By incorporating PSO and ABERA, the method aims to enhance the accuracy of detection models. Initial testing on standard biomedical imaging datasets shows promising results. This suggests potential for more efficient screening procedures and personalized treatment strategies. However, further research is necessary to validate these findings and explore additional aspects of oral cancer diagnosis and management.