Analyses of statistical feature fusion techniques in breast cancer detection

Abstract

ABSTRACT Breast cancer is one of the mortal diseases amongst women with increased incidences and mortality rate in every year globally. As its symptoms are not prominently noticeable in early stage, the early detection is difficult. Over the past four decades Mammography is used for diagnosing breast diseases. Most of CAD systems use either Cranio-Caudal or Medio-Lateral Oblique mammographic views. Radiologist will look at both the view for better diagnosis. To incorporate this perception with CAD, the detection performance of various statistical feature fusion in fusing the texture features of these two mammographic views are analysed in this work. The improved performance of accuracy: 97.5%, sensitivity: 100%, specificity: 97.2%, precision: 97.1%, F1 score: 96.23%, Mathews Correlation Coefficient: 0.952% and Balanced Classification Rate: 98.74% was achieved with Local Binary Pattern features fused through Canonical Correlation Analysis.