  Computer aided diagnosis (CAD) of Breast Cancer (BRCA) images has been an
active area of research in recent years. The main goals of this research is to
develop reliable automatic methods for detecting and diagnosing different types
of BRCA from diagnostic images. In this paper, we present a review of the state
of the art CAD methods applied to magnetic resonance (MRI) and mammography
images of BRCA patients. The review aims to provide an extensive introduction
to different features extracted from BRCA images through texture and
statistical analysis and to categorize deep learning frameworks and data
structures capable of using metadata to aggregate relevant information to
assist oncologists and radiologists. We divide the existing literature
according to the imaging modality and into radiomics, machine learning, or
combination of both. We also emphasize the difference between each modality and
methods strengths and weaknesses and analyze their performance in detecting
BRCA through a quantitative comparison. We compare the results of various
approaches for implementing CAD systems for the detection of BRCA. Each
approachs standard workflow components are reviewed and summary tables
provided. We present an extensive literature review of radiomics feature
extraction techniques and machine learning methods applied in BRCA diagnosis
and detection, focusing on data preparation, data structures, pre processing
and post processing strategies available in the literature. There is a growing
interest on radiomic feature extraction and machine learning methods for BRCA
detection through histopathological images, MRI and mammography images.
However, there isnt a CAD method able to combine distinct data types to provide
the best diagnostic results. Employing data fusion techniques to medical images
and patient data could lead to improved detection and classification results.
