Fuzzy Logic-Based Approaches in Medical Image Analysis: A Comprehensive Review
Keywords:
Classification, Disease diagnosis, Fuzzy logic, Medical image analysis, Segmentation, Uncertainty handlingAbstract
Medical image analysis plays a critical role in modern healthcare by aiding in disease diagnosis, treatment planning, and prognosis prediction. However, medical images often contain uncertainties and ambiguities due to noise, low contrast, and overlapping structures. Fuzzy logic-based approaches provide an effective solution to handle such uncertainties by offering a mathematical framework for reasoning with imprecise and vague data. This paper explores the application of fuzzy logic in various aspects of medical image analysis, including segmentation, classification, and feature extraction. The advantages of fuzzy logic, such as its ability to model human-like reasoning and incorporate expert knowledge, make it a powerful tool in improving the accuracy and robustness of medical image interpretation. Additionally, hybrid models integrating fuzzy logic with Genetic Algorithm, machine learning and deep learning techniques are discussed, highlighting their potential in enhancing automated diagnostic systems. The study concludes by outlining current challenges and future directions in the field, emphasizing the need for optimized fuzzy-based methodologies for real-time clinical applications.
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