Distance Analysis and Dimensionality Reduction using PCA on Brain Tumour MRI Scans

Aniket Jhariya; Dhvani Parekh; Joshua Lobo; Anupkumar Bongale; Ruchi Jayaswal; Prachi Kadam; Shruti Patil; Tanupriya Choudhury

doi:10.4108/eetpht.10.5632

Authors

Aniket Jhariya Symbiosis International University
Dhvani Parekh Symbiosis International University
Joshua Lobo Symbiosis International University
Anupkumar Bongale Symbiosis International University
Ruchi Jayaswal Symbiosis International University
Prachi Kadam Symbiosis International University
Shruti Patil Symbiosis International University
Tanupriya Choudhury Graphic Era University

DOI:

https://doi.org/10.4108/eetpht.10.5632

Keywords:

Dimensionality Reduction, Brain MRI Scans, Principal Component Analysis, Distance Analysis, Correlation Heatmap, Imaging Variance

Abstract

INTRODUCTION: Compression of MRI images while maintaining essential information, makes it easier to distinguish between different types of brain tumors. It also assesses the effect of PCA on picture representation modification and distance analysis between tumor classes.
OBJECTIVES: The objective of this work is to enhance the interpretability and classification accuracy of highdimensional MRI scans of patients with brain tumors by utilising Principle Component Analysis (PCA) to reduce their complexity.
METHODS:This study uses PCA to compress high-dimensional MRI scans of patients with brain tumors, focusing on improving classification using dimensionality reduction approaches and making the scans easier to understand.
RESULTS: PCA efficiently reduced MRI data, enabling better discrimination between different types of brain tumors and significant changes in distance matrices, which emphasize structural changes in the data.
CONCLUSION: PCA is crucial for improving the interpretability of MRI data.

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