Meta-Data Construction for Selection of Breast Tissue Biopsy Slides Image Classifier to Identify Ductal Carcinoma

Abstract

Currently there are large amounts of data available, to obtain useful information, multiple methods have been created to fulfill specific tasks, however, identifying the most appropriate method is often a difficult task. Meta-Learning is presented as an option that can recommend for new data the most appropriate method to perform a particular task based on experience, in which the features of the data and the performance of methods are related, this relationship is known as Meta-Data. Given the continuous increase of patients with breast cancer cases and availability of datasets, the images of slides of breast tissue biopsy to identify Ductal Carcinoma were selected as the object of study. The aim of this work is construction of Meta-Data that allows application of Meta-Learning for selection of the best Ductal Carcinoma identification method in the type of images under study. The proposed methodology presents a performance of the 99.6% accuracy, 99.9% AUC and 99.7% F-measure for Meta-Data Validation.