Background:
Breast cancer represents uncontrolled breast cell growth. Breast cancer is the most diagnosed
cancer in women worldwide. Early detection of breast cancer improves the chances of survival and increases treatment
options. There are various methods for screening breast cancer such as mammogram, ultrasound, computed tomography,
Magnetic Resonance Imaging (MRI). MRI is gaining prominence as an alternative screening tool for early detection and
breast cancer diagnosis. Nevertheless, MRI can hardly be examined without the use of a Computer-Aided Diagnosis
(CAD) framework, due to the vast amount of data.
Objective:
This paper aims to cover the approaches used in CAD system for the detection of breast cancer.
Method:
In this paper, the methods used in CAD systems are categories in two classes: the conventional approach and
artificial intelligence (AI) approach. The conventional approach covers the basic steps of image processing such as
preprocessing, segmentation, feature extraction and classification. The AI approach covers the various convolutional and
deep learning networks used for diagnosis.
Conclusion:
This review discusses some of the core concepts used in breast cancer and presents a comprehensive review
of efforts in the past to address this problem.