Improved uterine fibroid detection using the k-nearest neighbor algorithm
Improved uterine fibroid detection using the k-nearest neighbor algorithm
Abstract
Abstract Uterine fibroids are common benign tumors that affect a significant portion of women worldwide. Accurate and efficient detection of these growths is crucial for timely diagnosis and appropriate treatment. This study investigates the application of the K-Nearest Neighbor (KNN) algorithm for the detection of uterine fibroids in medical images. The KNN method, known for its simplicity and effectiveness in classification tasks, was employed to classify regions of interest (ROIs) within ultrasound images as either containing a fibroid or not. The performance of the KNN-based detection system was evaluated using a comprehensive dataset of labeled ultrasound images, and the results were compared to other commonly used machine learning techniques. The findings demonstrate that the KNN algorithm can achieve high accuracy, sensitivity, and specificity in detecting uterine fibroids, making it a promising tool for improving clinical decision-making and patient outcomes.
References
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