<![CDATA[Cancer detection and treatment is a major challenge in global health. Fuzzy logic has proven to be effective in overcoming uncertainty and ambiguity in medical data to improve the accuracy of cancer diagnosis. This article presents a review of the application of fuzzy logic in various cancer detection and treatment studies, including its use in the analysis of medical images such as CT scans and MRIs. Studies show that fuzzy logic not only improves cancer detection accuracy but also reduces the cost and time in the diagnosis process. The results suggest that a hybrid approach combining fuzzy logic with technologies such as artificial neural networks can significantly improve the efficiency and accuracy of cancer detection]]>
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