Novel K-Nearest Neighbor With Convolutional Neural Networks (KNN-CNN) For Accurate Brain Tumor Detection In Image Mining

Abstract

Brain tumor classification plays a crucial role in early diagnosis and effective treatment planning. In this paper, we propose a novel approach, K-Nearest Neighbor with Convolutional Neural Networks (KNN-CNN), for accurate brain tumor classification. The proposed method combines the strengths of K-Nearest Neighbor (KNN) and Convolutional Neural Networks (CNNs) to leverage both traditional feature-based classification and deep learning-based feature extraction. We use CNNs to learn high-level features from brain tumor images, and KNN is employed to classify tumors based on the extracted features. The experimental results on a brain tumor dataset demonstrate the effectiveness and efficiency of the KNN-CNN approach, achieving high classification accuracy and outperforming traditional methods.