An integrated multi-scale model for breast cancer histopathological image classification with joint colour-texture features

Abstract

Breast cancer is one of the most commonly diagnosed cancer in women worldwide, and is commonly diagnosed via histopathological microscopy imaging. Image analysis techniques aid physicians by automating some tasks involved in the diagnostic workflow. In this paper, we propose an integrated model that considers images at different magnifications, for classification of breast cancer histopathological images. Unlike some existing methods which employ a small set of features and classifiers, the present work explores various joint colour-texture features and classifiers to compute scores for the input data. The scores at different magnifications are then integrated. The approach thus highlights suitable features and classifiers for each magnification. Furthermore, the overall performance is also evaluated using the area under the ROC curve (AUC) that can determine the system quality based on patient-level scores. We demonstrate that suitable feature-classifier combinations can largely outperform the state-of-the-art methods, and the integrated model achieves a more reliable performance in terms of AUC over those at individual magnifications.