An Improved Image Mining Technique For Brain Tumour Classification Using Efficient Classifier

TL;DR

This paper introduces an enhanced image mining method utilizing pruned association rules and the MARI algorithm to classify brain CT scans into normal, benign, and malignant categories with high accuracy and sensitivity.

Contribution

It presents a novel combination of low-level image features and high-level expert knowledge using a pruned association rule approach for improved brain tumor classification.

Findings

Achieved 96% sensitivity in classification.

Achieved 93% accuracy in classification.

Effective assistance for physicians in diagnosis.

Abstract

An improved image mining technique for brain tumor classification using pruned association rule with MARI algorithm is presented in this paper. The method proposed makes use of association rule mining technique to classify the CT scan brain images into three categories namely normal, benign and malign. It combines the low level features extracted from images and high level knowledge from specialists. The developed algorithm can assist the physicians for efficient classification with multiple keywords per image to improve the accuracy. The experimental result on prediagnosed database of brain images showed 96 percent and 93 percent sensitivity and accuracy respectively.