Abstract
This systematic survey explores the landscape of fuzzy computer vision techniques in biomedical research using articles from the Scopus database over the past decade. With a focus on methodologies, applications, and challenges, the survey aims to guide future research at the intersection of fuzzy logic and computer vision in biomedicine. Emphasizing applications such as dental image analysis and brain tumor detection, the paper showcases the collaborative potential of deep learning and fuzzy logic in enhancing biomedical image analysis. Despite notable advancements, challenges like model interpretability and scalability persist. The survey concludes by proposing future research directions, underscoring the pivotal role of fuzzy computer vision in advancing biomedical research.