Automated breast cancer detection by reconstruction independent component analysis (RICA) based hybrid features using machine learning paradigms

Breast cancer (BC) masses and microcalcification are nonlinear with complex dynamics due to which radiologists fail to properly diagnose breast cancer. In this paper, we used a hybrid features extracting approach based on texture, morphological, Scale Invariant Feature Transform (SIFT), Gray Level Co-occurrence Matrix (GLCM), entropy, Elliptic Fourier Descriptors (EFDs), RICA, and sparse filtering methods. Various machine learning techniques have been employed to detect breast cancer, viz. Support Vector Machines (SVM), Decision Trees (DT), k-Nearest Neighbour, and Naive Bayes classifiers. The RICA-based feature set using SVM RBF has resulted in total accuracy of (94.88%), and ROC AUC = 0.9914. The hybrid features using RICA have been computed with other combinatorial logics. Moreover, the highest performance to detect BC based on the fusion of features was obtained with RICA with Textural features using SVM Gaussian kernel and yielded a total accuracy of (97.55%), and ROC AUC = 0.9976. The hybrid features with RICA were found to yield the highest detection performance. It is revealed that the new feature-extracting approach can be useful for the early detection of breast cancer by physicians to decrease the overall mortality rate. The methods will be very useful for treatment modification to achieve better clinical outcomes.