A New Hybrid Approach for Medical Image Classification

This paper discusses the application of data mining for the classification of a medical image. A hybrid technique for brain tumor detection using UpDown Directed Acyclic Graph (UDDAG) association rule with ID3 decision tree classifier is applied. This hybrid approach classifies the CT scan brain images into three categories namely normal, benign and malignant. The major steps involved in the technique are: pre-processing, feature extraction, association rule mining and classification. The pre-processing step has been done using the 2D median filtering process. The edge features from the image has been extracted using canny edge detection technique. The sequential patterns are generated by UpDown Directed Acyclic Graph (UDDAG) algorithm that mines the Association Rule. The decision tree method (ID3) has been used to classify the medical images for diagnosis based on the rules generated by the association rule. This hybrid approach (HARC) enhances the efficiency and accuracy of the brain tumor detection from the CT scan brain images.