Methods Used in Computer-Aided Diagnosis for Breast Cancer Detection Using Mammograms: A Review

Abstract

According to the American Cancer Society’s forecasts for 2019, there will be about 268,600 new cases in the United States with
invasive breast cancer in women, about 62,930 new noninvasive cases, and about 41,760 death cases from breast cancer. As a
result, there is a high demand for breast imaging specialists as indicated in a recent report for the Institute of Medicine and National
Research Council. One way to meet this demand is through developing Computer-Aided Diagnosis (CAD) systems for
breast cancer detection and diagnosis using mammograms. This study aims to review recent advancements and developments
in CAD systems for breast cancer detection and diagnosis using mammograms and to give an overview of the methods used in
its steps starting from preprocessing and enhancement step and ending in classification step. The current level of performance
for the CAD systems is encouraging but not enough to make CAD systems standalone detection and diagnose clinical systems.
Unless the performance of CAD systems enhanced dramatically from its current level by enhancing the existing methods, exploiting
new promising methods in pattern recognition like data augmentation in deep learning and exploiting the advances in
computational power of computers, CAD systems will continue to be a second opinion clinical procedure.