A Combined Framework for Medical Image Classification and Detection of Brain Abnormalities Utilizing K-Nearest Neighbors and an Enhanced Convolutional Neural Network
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Abstract: The detection of abnormalities in medical images plays a pivotal role in early diagnosis and treatment. This paper presents a hybrid approach that combines K-Nearest Neighbors (KNN) and deep learning techniques to improve medical image classification and anomaly detection. The method applies KNN for classifying images such as MRI, CT, and X-ray scans, focusing on abnormality detection in brain images. By integrating KNN classifiers with feature extraction methods, the approach addresses challenges such as class imbalance and small datasets, resulting in improved detection accuracy. The effectiveness of the proposed method is demonstrated on a medical image dataset, showing significant improvements in both classification and anomaly detection tasks.
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