Exploring Bayesian networks for automated breast cancer detection

Abstract

This paper gives an introduction to the Bayesian networks for the exploration of implementing a Bayesian belief network for an automated breast cancer detection support tool. It is intuitive that Bayesian networks can be employed as one viable option for computer-aided detection by representing the relationships between diagnoses, physical findings, laboratory test results, and imaging study findings. This paper brings important entities such as Radiologists, Image Processing Scientists, Data Base Specialists and Applied Mathematicians on a common platform. A brief background concerning causal networks, probability theory and Bayesian networks is given. Available computational tools and platforms are described. Steps towards building a Bayesian Belief Network Implementation are introduced.