A fully integrated computer-aided diagnosis system for digital X-ray mammograms via deep learning detection, segmentation, and classification
A fully integrated computer-aided diagnosis system for digital X-ray mammograms via deep learning detection, segmentation, and classification
复制标题
一个完全集成的计算机辅助诊断系统，通过深度学习检测、分割和分类实现数字X射线乳腺X射线摄影
DOI：
10.1016/j.ijmedinf.2018.06.003
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发表时间：
2018-09-01
影响因子：
4.9
通讯作者：
Kim, Tae-Seong
中科院分区：
文献类型：
--
作者：
Al-antari, Mugahed A.;Al-masni, Mohammed A.;Kim, Tae-Seong
计算机辅助诊断(CAD)系统需要在一个框架内进行检测、分割和分类，以帮助放射科医生有效地进行准确诊断。针对乳腺肿块的检测、分割和分类问题，提出了一种完全集成的基于深度学习的数字化乳腺X线片筛选系统，采用区域深度学习方法YOLO(You-Only-Look-Once)对整个乳腺肿块进行检测。提出并利用了一种新的深度网络模型--全分辨率卷积网络(FRCN)对海量图像进行分割。最后，使用深度卷积神经网络(CNN)来识别肿块，并将其分类为良性或恶性。为了评估所提出的集成CAD系统在检测、分割和分类方面的准确性，使用了公开可用的带注释的INBAME数据库。通过四次交叉验证测试，该系统的质量检测准确率为98.96%，Matthews相关系数(MCC)为97.62%，F1-Score为99.24%。此外，FRCN的肿块分割结果总体准确率为92.97%，MCC为85.93%，Dice(F1-Score)为92.69%，Jaccard相似系数度量为86.37%。通过CNN对检出和分割的肿块进行分类，总体准确率为95.64%，AUC为94.78%，MCC为89.91%，F1评分为96.84%。结果表明，在检测、分割和分类的所有阶段，所提出的CAD系统的性能都优于最新的传统深度学习方法。我们提出的CAD系统可用于辅助放射科医生对乳腺肿块进行检测、分割和分类的所有阶段。
A computer-aided diagnosis (CAD) system requires detection, segmentation, and classification in one framework to assist radiologists efficiently in an accurate diagnosis. In this paper, a completely integrated CAD system is proposed to screen digital X-ray mammograms involving detection, segmentation, and classification of breast masses via deep learning methodologies.In this work, to detect breast mass from entire mammograms, You-Only-Look-Once (YOLO), a regional deep learning approach, is used. To segment the mass, full resolution convolutional network (FrCN), a new deep network model, is proposed and utilized. Finally, a deep convolutional neural network (CNN) is used to recognize the mass and classify it as either benign or malignant. To evaluate the proposed integrated CAD system in terms of the accuracies of detection, segmentation, and classification, the publicly available and annotated INbreast database was utilized. The evaluation results of the proposed CAD system via four-fold cross-validation tests show that a mass detection accuracy of 98.96%, Matthews correlation coefficient (MCC) of 97.62%, and F1-score of 99.24% are achieved with the INbreast dataset. Moreover, the mass segmentation results via FrCN produced an overall accuracy of 92.97%, MCC of 85.93%, and Dice (F1-score) of 92.69% and Jaccard similarity coefficient metrics of 86.37%, respectively. The detected and segmented masses were classified via CNN and achieved an overall accuracy of 95.64%, AUC of 94.78%, MCC of 89.91%, and F1-score of 96.84%, respectively. Our results demonstrate that the proposed CAD system, through all stages of detection, segmentation, and classification, outperforms the latest conventional deep learning methodologies. Our proposed CAD system could be used to assist radiologists in all stages of detection, segmentation, and classification of breast masses.