Enhancing Medical Image Recognition through Fuzzy Logic-Based Uncertainty Modeling in Artificial Intelligence Systems

Medical image recognition has become a critical component in modern healthcare systems, enabling early diagnosis and improving clinical decision-making.However, traditional artificial intelligence (AI) models, particularly deep learning approaches, often struggle to handle uncertainty, ambiguity, and imprecision inherent in medical imaging data.This limitation may lead to reduced interpretability and reliability in real-world clinical environments.To address these challenges, this study proposes a fuzzy logic-based uncertainty modeling framework integrated with artificial intelligence for enhanced medical image recognition.The proposed approach leverages fuzzy sets and linguistic variables to represent uncertain and vague information present in medical images, such as variations in tissue boundaries, noise, and low contrast regions.By incorporating fuzzy inference mechanisms into AI-based recognition pipelines, the system can mimic human-like reasoning and provide more flexible and interpretable decision outputs.The methodology includes the definition of input features extracted from medical images, the design of appropriate membership functions, and the construction of a comprehensive fuzzy rule base for classification and diagnosis tasks.Experimental results demonstrate that the integration of fuzzy logic significantly improves recognition accuracy, robustness, and interpretability compared to conventional AI models.Moreover, the proposed system enhances transparency in decision-making, which is crucial for clinical acceptance and trust.This study contributes to the development of intelligent, reliable, and explainable medical image recognition systems by effectively handling uncertainty through fuzzy logic-based modeling.