CWLA: A novel cognitive classifier for breast mass diagnosis
Amir Tahmasbi, Forough Saki, Shahriar B. Shokouhi · 2011
A novel cognitive classifier has been introduced to develop a trustable mammography Computer Aided Diagnosis (CADx) system which is called Cognitive Weighted Linear Aggregation (CWLA). A group of in-depth analyzed features are extracted from the preprocessed Regions of Interest (ROIs) and mapped from set of real numbers to a set of linguistic terms. The proposed classifier primes a knowledge base which is developed according to a mammography expert. The semantic comparison of the extracted features with the expectations of the knowledge base, which is called cognitive resonance, leads to a primary clustering. Finally, the linguistic terms are remapped onto the set of real numbers and the final assessment comes out from the weighted linear aggregation of clustered categories. Since the output of the system comes with reason, the system is reliable. The achieved area under Receiver Operational Characteristics (ROC) curve (Az) and False Positive Rate (FPR) are 0.858 and 5.26%, respectively.