Breast detection in X-ray mastography with machine learning techniques

Abstract

Breast cancer is one of the leading causes of death in women in the world. An early detection is crucial to be able to performan appropriatetreatment and thus reduce mortality. An importanttool for early detection ismastography, whichis a technique to obtainimages through X-rays. Through this technique it is possible to detect abnormalities in the breast tissue, thus allowing to detect the disease at an early stage.On the other hand,machine learning algorithmshaveprovidedpromising resultsin automaticdetection.In this work we combine both X-ray mastography and machine learning algorithms to perform breast detection.The YOLO neural network was used, which is a convolutional network that performs the detection in a single stage. For the training of the network, 216 mastographiesfrom the dataset of breast mammography images with masses were used. Experimental resultsin position-independent breast detection showedan average confidence of 0.983, and a standard deviation of 0.024 in a sample of 200 images.This result allows to propose YOLO as an accurate preprocessing method for more sophisticated neural networks designed to solve breast cancer related tasks.