An Efficient Diagnosis of Kidney Images Using Association Rules

This paper is focusing to develop a method based on association rule-mining to enhance the diagnosis of ultrasound kidney images. This is to implement a computer-aided decision support system for an automated diagnosis and classification of kidney images. The method uses association rule mining to analyze the medical images and automatically generates suggestions of diagnosis. It combines automatically extracted low-level features from images with high-level knowledge given by a specialist in order to suggest the diagnosing. Association rule mining is a well-known data mining technique that aims to find interesting patterns in very large databases. The proposed method distinguishes three kidney categories namely normal, cortical cyst and medical renal. The segmented US kidney images are taken for processing. Then feature extraction process is applied to extract the features and from that extracted features only the relevant features are selected. So feature selection and discretization process is done on the extracted features that reduce the mining complexity. The association rules are generated based on the selected features. The proposed method uses the algorithm Bayesian Classifier which is a new associative classifier based on Bayesian theorem. This classifies the given image with high values of accuracy to suggest a diagnosis.