Paper Info
Title
Hybrid Medical Image Classification Using Association Rule Mining with Decision Tree Algorithm
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.
Year
Venue
Keywords
2010
Clinical Orthopaedics and Related Research
association rule,feature extraction,decision tree,median filter,ct scan,association rule mining,brain imaging,edge detection,medical diagnosis,pattern recognition
Field
DocType
Volume
Canny edge detector,Decision tree,Data mining,Median filter,Computer science,Artificial intelligence,Contextual image classification,Pattern recognition,Feature extraction,Association rule learning,Medical diagnosis,Machine learning,Decision tree learning
Journal
abs/1001.3
ISSN
Citations 
PageRank 
Journal of Computing, Vol. 2, Issue 1, January 2010
8
0.71
References 
Authors
2
2
Name
Order
Citations
PageRank
P. Rajendran1325.01
M. Madheswaran210215.57