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

• Published in: Multimedia tools and applications Vol. 81; no. 13; pp. 18595 - 18616
• Main Authors: Saeed, Soobia, Abdullah, Afnizanfaizal, Jhanjhi, N. Z., Naqvi, Mehmood, Nayyar, Anand
• Format: Journal Article
• Language: English
• Published: New York Springer US 01-05-2022; Springer Nature B.V
• Subjects: Algorithms; Brain; Brain cancer; Cerebrospinal fluid; Computer Communication Networks; Computer Science; Damage detection; Data Structures and Information Theory; Fast Fourier transformations; Fourier transforms; Image quality; K-nearest neighbors algorithm; Laplace transforms; Light; Magnetic resonance imaging; Medical imaging; Multimedia Information Systems; Special Purpose and Application-Based Systems; Tumors; K-Nearest Neighbor; MRI; Brain Tumor; Laplacian
• ISSN: 1380-7501; 1573-7721
• DOI: 10.1007/s11042-022-12271-x

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.