Abstract
Brain tumors pose significant challenges in the medical domain, necessitating advanced diagnostic techniques for early and accurate detection. This research paper presents a comprehensive study on the application of the K-Nearest Neighbors (KNN) algorithm to a dataset comprising brain tumor images. The methodology involved segmenting the images using the Canny method, extracting relevant features via Hu Moments, and subsequently employing the KNN algorithm for classification. Using a 5-fold cross-validation, the system consistently achieved an average accuracy of approximately 62%. These findings highlight the potential of traditional machine learning algorithms in medical imaging, providing valuable insights for both researchers and practitioners. While the results are promising, the study also underscores the importance of integrating such algorithms with other diagnostic methods for optimal results
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