  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.
