Variations On Breast Density And Subtlety Of The Findings Require Different Computational Intelligence Pipelines For The Diagnosis Of Clustered Microcalcifications

In this work, we study the factors that influence the efficacy of a proposed Computer Aided Diagnosis (CADx) framework for the diagnosis of clustered microcalcifications (MCs) using a large dataset of mammograms containing cases of varying breast density and findings' subtlety. The reported results indicate that the proposed framework performs towards the right direction, as it appears high classification performance (A(z)=0.909) for specific subsets of cases, while outperforming at the same time the performance of the radiologists who evaluated the same cases. The effect of the initial enhancement of mammograms in the CADx pipeline is then investigated, by applying three different image enhancement techniques on several subsets of mammograms. We observed that for the considered subsets of dense mammograms, a wavelet-based enhancement algorithm outperformed the rest and provided superior classification performance (A(z)=0.849). We indicate therefore that the density of the breast determines the need of different computational algorithms for the analysis of a mammogram and as a result the a priori knowledge of this factor may be exploited for the optimization of the diagnostic process.