An intelligent mining system for diagnosing medical images using combined texture-histogram features
The aim of this article is to design an expert system for medical image diagnosis. We propose a method based on association rule mining combined with classification technique to enhance the diagnosis of medical images. This system classifies the images into two categories namely benign and malignant. In the proposed work, association rules are extracted for the selected features using an algorithm called AprioriTidImage, which is an improved version of Apriori algorithm. Then, a new associative classifier CLASS_Hiconst (CLassifier based on ASSociation rules with High Confidence and Support) is modeled and used to diagnose the medical images. The performance of our approach is compared with two different classifiers Fuzzy-SVM and multilayer back propagation neural network (MLPNN) in terms of classifier efficiency with sensitivity, specificity, accuracy, positive predictive value, and negative predictive value. The experimental result shows 96% accuracy, 97% sensitivity, and 96% specificity and proves that association rule based classifier is a powerful tool in assisting the diagnosing process.  2013 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 23, 194203, 2013
association rule mining associative classifier brain tumor image processing