Dehghan Rouzi Mohammad, Moshiri Behzad, Khoshnevisan Mohammad, Akhaee Mohammad Ali, Jaryani Farhang, Salehi Nasab Samaneh, Lee Myeounggon
School of Electrical and computer Engineering, College of Engineering, University of Tehran, Tehran 14174-66191, Iran.
Department of Electrical and Computer Engineering, University of Waterloo, Ontario, ON N2L 3G1, Canada.
J Imaging. 2023 Nov 13;9(11):247. doi: 10.3390/jimaging9110247.
Breast cancer's high mortality rate is often linked to late diagnosis, with mammograms as key but sometimes limited tools in early detection. To enhance diagnostic accuracy and speed, this study introduces a novel computer-aided detection (CAD) ensemble system. This system incorporates advanced deep learning networks-EfficientNet, Xception, MobileNetV2, InceptionV3, and Resnet50-integrated via our innovative consensus-adaptive weighting (CAW) method. This method permits the dynamic adjustment of multiple deep networks, bolstering the system's detection capabilities. Our approach also addresses a major challenge in pixel-level data annotation of faster R-CNNs, highlighted in a prominent previous study. Evaluations on various datasets, including the cropped DDSM (Digital Database for Screening Mammography), DDSM, and INbreast, demonstrated the system's superior performance. In particular, our CAD system showed marked improvement on the cropped DDSM dataset, enhancing detection rates by approximately 1.59% and achieving an accuracy of 95.48%. This innovative system represents a significant advancement in early breast cancer detection, offering the potential for more precise and timely diagnosis, ultimately fostering improved patient outcomes.
乳腺癌的高死亡率通常与诊断延迟有关，乳房X光检查是早期检测的关键工具，但有时也存在局限性。为了提高诊断的准确性和速度，本研究引入了一种新型的计算机辅助检测（CAD）集成系统。该系统通过我们创新的共识自适应加权（CAW）方法，整合了先进的深度学习网络——EfficientNet、Xception、MobileNetV2、InceptionV3和Resnet50。这种方法允许对多个深度网络进行动态调整，增强了系统的检测能力。我们的方法还解决了先前一项重要研究中强调的快速R-CNN像素级数据标注中的一个主要挑战。在包括裁剪后的数字乳房X线摄影筛查数据库（DDSM）、DDSM和INbreast等各种数据集上的评估证明了该系统的卓越性能。特别是，我们的CAD系统在裁剪后的DDSM数据集上表现出显著改进，检测率提高了约1.59%，准确率达到95.48%。这种创新系统代表了早期乳腺癌检测的重大进展，为更精确、及时的诊断提供了可能，最终改善患者的治疗效果。
