A novel triangular geometry-based automatic pectoral muscle removal approach for breast cancer detection and classification

According to recent studies, the second largest cause of cancer-related fatalities among women is breast cancer. However, the earlier detection might remarkably increase the survival rates of the patients. Therefore, in this paper, an efficient and robust triangular geometry-based pectoral muscle removal approach is proposed. The motivation of the proposed approach is to improve detection and classification accuracy in two aspects: (i) the pre-processing methods associated with the segmentation and localisation of the affected area, and (ii) the accuracy of the features extracted to categorise as the normal, benign and malignant classes. The variance-weighted average filter-based image denoising and pixel-level image self-fusion method performs robust pre-processing for varying breast densities and preserves fine details. Moreover, a novel angle-based triangular geometry pectoral muscle removal approach with an automatic optimal step length-based multi-adaptive Otsu thresholding is used for improved segmentation. Feature extraction and hybrid optimal feature selection using an adaptive weighted objective function are also introduced. Further, the classification is performed with a hybrid ensemble classifier using a majority voting rule and Bayesian optimisation technique. The experimentations show the classification accuracy of 91.61%, 94.1%, sensitivity 90.77%, 94.87% and specificity of 81.58%, 94.25% for multiclass classification for MIAS and DDSM datasets, respectively. Moreover, an AUC of 0.99 on the ROC curve demonstrate an excellent performance and good diagnostic accuracy in differentiating between benign, malignant, and normal cases of breast cancer.