Feature Engineering for the Subtype Classification of Breast Cancer: A Model Incorporating DCE and DWI Images

For the 4-IHC classification task, the best accuracy of 78.4% was achieved based on linear discriminant analysis (LDA) or subspace discrimination of assembled learning in conjunction with 25 selected features, and only small dependent emphasis of Kendall-tau-b for sequential features based on the DWI images (DWIsequential) with the LDA model yielding an accuracy of 53.7%. The subspace discriminant of ensembled learning using eight features yielded the highest accuracy of 91.8% for comparing TN to non-TN cancers, and the maximum variance for DWIsequential alone together with a linear support vector machine (SVM) model achieved an accuracy of 83.6%.