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All Journal International Journal of Artificial Intelligence in Medical Issues
Thomas Edyson Tarigan
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Computer Science & IT Dentistry Health Professions Medicine & Pharmacology Public Health

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Application of the K-Nearest Neighbors (KNN) Algorithm on the Brain Tumor Dataset Najwaini, Effan; Thomas Edyson Tarigan; Fajri Profesio Putra; Sulistyowati
International Journal of Artificial Intelligence in Medical Issues Vol. 1 No. 1 (2023): International Journal of Artificial Intelligence in Medical Issues
Publisher : Yocto Brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijaimi.v1i1.85

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
Co-Authors Effan Najwaini Fajri Profesio Putra Sulistyowati