Automated Mammogram Classification Using a Multi-Resolution Pattern Recognition Approach
SIBGRAPI '01 Proceedings of the 14th Brazilian Symposium on Computer Graphics and Image Processing
A New Method for Image Classification by Using Multilevel Association Rules
ICDEW '05 Proceedings of the 21st International Conference on Data Engineering Workshops
Bi-level weights sum method for shock diagnosis
Expert Systems with Applications: An International Journal
An approach for adaptive associative classification
Expert Systems with Applications: An International Journal
Evaluating Cluster Preservation in Frequent Itemset Integration for Distributed Databases
Journal of Medical Systems
Classification based on specific rules and inexact coverage
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Mining frequent patterns and association rules using similarities
Expert Systems with Applications: An International Journal
Hi-index | 12.06 |
In this paper, we present a novel method for the classification of mammograms using a unique weighted association rule based classifier. Images are preprocessed to reveal regions of interest. Texture components are extracted from segmented parts of the image and discretized for rule discovery. Association rules are derived between various texture components extracted from segments of images and employed for classification based on their intra- and inter-class dependencies. These rules are then employed for the classification of a commonly used mammography dataset, and rigorous experimentation is performed to evaluate the rules' efficacy under different classification scenarios. The experimental results show that this method works well for such datasets, incurring accuracies as high as 89%, which surpasses the accuracy rates of other rule based classification techniques.