Novel Framework for Breast Cancer Classification for Retaining Computational Efficiency and Precise Diagnosis

Abstract

Classification of breast cancer is still an open-end challenge in medical image processing. The existing literatures were reviewed to found that existing solution are more pivotal towards accuracy in classification and less towards achieving computational effectiveness in classification process. Therefore, this paper presents a novel classification approach that bridges the trade-off between computational performances of classifier with its final response towards disease criticality. An analytical framework is built that takes the input of Magnetic Resonance Imaging (MRI) of breast cancer which is subjected to non-linear map-based filter for enhancing pre-processing operation. The algorithm also offers a novel integral transformation scheme that lets the filtered image to get itself transformed followed by precise extraction of foreground and background for assisting in reliable classification. A statistical-based approach is used for extracting feature followed by classifying using unsupervised learning algorithm. The study outcome shows superior performance compared to existing schemes of classification.