Towards Automatic Classification of Breast Cancer Histopathological Image

Today the treatment and diagnosis of diseases heavily rely on medical images. These images are produced in huge amount, which causes a bottleneck in the process of investigation. One of the most important diseases, which heavily rely on images, is Breast Cancer. We introduce a classification system based on a hybrid feature extractor that relies on Completed Local Binary Pattern (CLBP), Singular Value Decomposition (SVD), Gabor Filter, Wavelet Transform and Support Vector Machines classifier (SVM). The purpose of this research is to increase the level of classification automation of Breast Cancer (BC) Histopathological image. The Experimental approach was used to investigate the effect of the proposed algorithm which has shown promising results. These results were benchmarked against a standard dataset of BC Histopathological image.