PATTERNS OUT OF CASES USING KOHONEN MAPS IN BREAST CANCER DIAGNOSIS

Abstract

DESMAI is a framework for helping experts in breast cancer diagnosis. It allows experts to explore digital mammographic image databases according to a certain topology criteria when they need to decide whether a sample is benign or malignant. In this way, they are provided with complementary information to enhance their interpretations and predictions. The core of the application is a SOMCBR system, which is variant of a Case-Based Reasoning system featured by organizing the case memory using a Self-Organizing Map. The article presents a strategy for improving the SOMCBR reliability thanks to the relations between cases and clusters. The approach is successfully applied in DESMAI for estimating, if it is possible, the class of the recovered mammographies.