Hybrid Medical Image Classification Using Association Rule Mining with Decision Tree Algorithm

TL;DR

This paper presents a hybrid image classification system combining association rule mining and decision trees to improve accuracy in diagnosing brain tumors from CT images, achieving 97% sensitivity and 95% accuracy.

Contribution

It introduces a novel hybrid approach integrating FP-Tree based association rule mining with decision trees for enhanced brain tumor classification in medical images.

Findings

Achieved 97% sensitivity in tumor detection.

Achieved 95% overall classification accuracy.

Improved classification efficiency over traditional methods.

Abstract

The main focus of image mining in the proposed method is concerned with the classification of brain tumor in the CT scan brain images. The major steps involved in the system are: pre-processing, feature extraction, association rule mining and hybrid classifier. The pre-processing step has been done using the median filtering process and edge features have been extracted using canny edge detection technique. The two image mining approaches with a hybrid manner have been proposed in this paper. The frequent patterns from the CT scan images are generated by frequent pattern tree (FP-Tree) algorithm that mines the association rules. The decision tree method has been used to classify the medical images for diagnosis. This system enhances the classification process to be more accurate. The hybrid method improves the efficiency of the proposed method than the traditional image mining methods. The experimental result on prediagnosed database of brain images showed 97% sensitivity and 95% accuracy respectively. The physicians can make use of this accurate decision tree classification phase for classifying the brain images into normal, benign and malignant for effective medical diagnosis.