Department of Industrial Engineering, German Jordanian University, Mushaqar 11180, Amman, Jordan.
J Healthc Eng. 2020 Mar 12;2020:9162464. doi: 10.1155/2020/9162464. eCollection 2020.
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.
根据美国癌症协会 2019 年的预测，美国女性中浸润性乳腺癌新发病例约为 268600 例，非浸润性新发病例约为 62930 例，乳腺癌死亡病例约为 41760 例。因此，正如医学研究所和国家研究委员会最近的一份报告所指出的，对乳腺成像专家的需求很高。满足这一需求的一种方法是使用乳房 X 线照片开发用于乳腺癌检测和诊断的计算机辅助诊断 (CAD) 系统。本研究旨在回顾使用乳房 X 线照片进行乳腺癌检测和诊断的 CAD 系统的最新进展和发展，并概述其步骤中使用的方法，从预处理和增强步骤开始，最后到分类步骤。CAD 系统的当前性能水平令人鼓舞，但还不足以使 CAD 系统成为独立的检测和诊断临床系统。除非 CAD 系统的性能通过增强现有方法从当前水平显著提高，或者利用数据增强等深度学习中的新的有前途的模式识别方法，并利用计算机计算能力的进步，否则 CAD 系统将继续作为辅助临床决策的一种方法。
