New techniques for efficiently k-NN algorithm for brain tumor detection

Abstract

The k-NN algorithm missing values is one of the current research issues, especially in 4D frequency. This study addresses the accuracy of the images, increases the efficiency of missing k-NN hybrid values, and constructs a research framework that can identify cancer-damaged areas isolated from non-tumors areas using 4D image light field tools. Additionally, we propose a new approach to detect brain tumors or cerebrospinal fluid (CSF) development in the early stages of formation. We apply a combination of the hybrid K-Nearest Neighbor (k-NN) algorithm, Fast Fourier Transform, and the Laplace Transform techniques on four-dimensional (4D) MRI (Magnetic Resonance Imaging) images. These approaches use a 4D modulation method that dictates the light field used for the Light Editing Field (LEF) tool. Depending on the user’s input, an objective evaluation of each ray is calculated using the k-NN method to maintain the 4D frequency redundant light fields. We suggest that light field methods can improve the quality of images through LEF since the light field composite pipelines reduce the borders of artifacts.