Background and Objective

Deep learning detection and classification from medical imagery are key components for computer-aided diagnosis (CAD) systems to efficiently support physicians leading to an accurate diagnosis of breast lesions.

Methods

In this study, an integrated CAD system of deep learning detection and classification is proposed aiming to improve the diagnostic performance of breast lesions. First, a deep learning YOLO detector is adopted and evaluated for breast lesion detection from entire mammograms. Then, three deep learning classifiers, namely regular feedforward CNN, ResNet-50, and InceptionResNet-V2, are modified and evaluated for breast lesion classification. The proposed deep learning system is evaluated over 5-fold cross-validation tests using two different and widely used databases of digital X-ray mammograms: DDSM and INbreast.

Results

The evaluation results of breast lesion detection show the capability of the YOLO detector to achieve overall detection accuracies of 99.17% and 97.27% and F1-scores of 99.28% and 98.02% for DDSM and INbreast datasets, respectively. Meanwhile, the YOLO detector could predict 71 frames per second (FPS) at the testing time for both DDSM and INbreast datasets. Using detected breast lesions, the classification models of CNN, ResNet-50, and InceptionResNet-V2 achieve promising average overall accuracies of 94.50%, 95.83%, and 97.50%, respectively, for the DDSM dataset and 88.74%, 92.55%, and 95.32%, respectively, for the INbreast dataset.

Conclusion

The capability of the YOLO detector boosted the classification models to achieve a promising breast lesion diagnostic performance. Such prediction results should help to develop a feasible CAD system for practical breast cancer diagnosis.

中文翻译：

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评估深度学习检测和分类，以计算机辅助诊断X线乳房X线照片中的乳腺病变。

背景与目的

医学图像的深度学习检测和分类是计算机辅助诊断（CAD）系统的关键组件，可有效支持医生，从而对乳腺病变进行准确的诊断。

方法

在这项研究中，提出了一个深度学习检测和分类的集成CAD系统，旨在提高乳腺病变的诊断性能。首先，采用深度学习YOLO检测器，并从整个乳房X线照片评估乳房病变的评估。然后，对三个深度学习分类器（即常规前馈CNN，ResNet-50和InceptionResNet-V2）进行了修改，并对乳腺病变分类进行了评估。使用两个不同且广泛使用的数字X射线乳房X线照片数据库：DDSM和INbreast，通过5倍交叉验证测试对拟议的深度学习系统进行了评估。

结果

乳腺病变检测的评估结果表明，对于DDSM和INbreast数据集，YOLO检测器能够分别实现99.17％和97.27％的整体检测准确度以及F1分数分别为99.28％和98.02％的检测能力。同时，对于DDSM和INbreast数据集，YOLO检测器可以在测试时预测每秒71帧（FPS）。使用检测到的乳腺病变，CNN，ResNet-50和InceptionResNet-V2的分类模型对于DDSM数据集分别达到94.50％，95.83％和97.50％的有希望的平均总体准确度，分别为88.74％，92.55％和95.32。 ％，分别用于INbreast数据集。

结论

YOLO检测器的功能增强了分类模型，从而实现了有希望的乳腺病变诊断性能。这样的预测结果应有助于为实际的乳腺癌诊断开发可行的CAD系统。