Brain tumor MRI image classification with feature selection and extraction using linear discriminant analysis

TL;DR

This paper presents a novel feature selection and extraction method using PCA and LDA for MRI brain tumor classification, achieving higher accuracy with a larger dataset than previous studies.

Contribution

It introduces a combined feature selection and extraction approach using intensity, texture, and shape features, improving classification robustness and accuracy.

Findings

Effective feature reduction with PCA and LDA.

Higher classification accuracy on a large MRI dataset.

Comparison shows linear LDA outperforms nonlinear methods.

Abstract

Feature extraction is a method of capturing visual content of an image. The feature extraction is the process to represent raw image in its reduced form to facilitate decision making such as pattern classification. We have tried to address the problem of classification MRI brain images by creating a robust and more accurate classifier which can act as an expert assistant to medical practitioners. The objective of this paper is to present a novel method of feature selection and extraction. This approach combines the Intensity, Texture, shape based features and classifies the tumor as white matter, Gray matter, CSF, abnormal and normal area. The experiment is performed on 140 tumor contained brain MR images from the Internet Brain Segmentation Repository. The proposed technique has been carried out over a larger database as compare to any previous work and is more robust and effective. PCA and Linear Discriminant Analysis (LDA) were applied on the training sets. The Support Vector Machine (SVM) classifier served as a comparison of nonlinear techniques Vs linear ones. PCA and LDA methods are used to reduce the number of features used. The feature selection using the proposed technique is more beneficial as it analyses the data according to grouping class variable and gives reduced feature set with high classification accuracy.