GLCM based Improved Mammogram Classification using Associative Classifier

Abstract

Abstract-Among women, 12% possibility of developing a breast cancer and 3.5% possibility of mortality due to this cause is reported [1]. Nowadays early detection of breast cancer became very important. Mammogram -a breast X-ray is used to investigate and diagnose breast cancer. In this paper, authors propose GLCM (Grey Level Co-occurrence Matrix) feature based improved mammogram classification using an associative classifier. Mining of association rules from mammogram dataset discovers frequently occurring patterns. It depends on user specified minimum confidence and support value. This dependency causes an increase in search space. The authors propose two-phase optimization procedure to overcome these limitations.The initial phase comprises feature optimization by adopting proposed PreARM (Pre-processing step for Association Rule Mining) method. The next phase comprises association rule optimization by adopting proposed ESAR (Extraction of Strong Association Rules) method to generate efficient, highly correlated and robust rules. Proposed associative classification method is substantiated by adapting authentic MIAS and DDSM mammogram database. The experimentation concedes 91% and 90% trimming of GLCM features and association rules by adopting PreARM and ESAR algorithms respectively. Using optimized association rules, the classification accuracies procured for MIAS and DDSM datasets are 92% and 94% respectively. Area under Receiver Operating Characteristic (ROC) curves obtained by proposed system for MIAS and DDSM datasets are 0.9656 and 0.9285 respectively. Results of GLCM based associative classifier are compared with GLCM based Random Forest (RF), an ensemble learning method. The experimental result shows that GLCM based associative classifier outperforms RF method with respect to accuracy and AUC, and it is a promising method for mammogram classification.

Using association rule mining and ontologies to generate metadata recommendations from multiple biomedical databases

“…In the context of the existing literature, our method belongs to a category of supervised learning models known as associative classifiers. This technique was first published in 1998 (15), and it has been broadly investigated and exploited by the data mining and machine learning communities in a number of successful real-word applications (16)(17)(18)(19). Associative classifiers use association rule mining to extract interesting rules from the training data, and the extracted rules are used to build a classifier.…”