Enhancing Lung Cancer Diagnostics with Fuzzy Logic: A Comprehensive Review

Lung cancer still accounts for the most significant number of cancer deaths worldwide, mainlybecause detection occurs at later stages, and people do not gain early access to diagnostic tools.Conventional detection methods are effective but do not provide adequate precision for staging or gradingbefore timely intervention becomes possible. In this review, fuzzy logic will be discussed to get a robustmethod for better lung cancer detection and staging by tumor grading. Fuzzy logic systems help model theuncertainties and complexities of medical data, like tumor size, shape, texture, and biomarker levels. Thisfacilitates much more considerable detail in comparison with the traditional methods. By accuratelyclassifying stages of lung cancer based on non-invasive and fully automated methods utilizing fuzzy rulebased systems, doctors could make better judgments. This paper discusses the concept of fuzzy logic, itsusage in lung cancer detection and analysis, and further advantages over previous methods. We reviewrecent advancements, challenges, and prospects in adopting fuzzy logic for medical diagnostics, bridgingthe gap between computational intelligence and clinical needs for promising solutions to improve lungcancer outcomes.