Aim

In a pilot study to improve detection of malignant lesions in breast mammograms, we aimed to develop a new method called BDR-CNN-GCN, combining two advanced neural networks: (i) graph convolutional network (GCN); and (ii) convolutional neural network (CNN).

Method

We utilised a standard 8-layer CNN, then integrated two improvement techniques: (i) batch normalization (BN) and (ii) dropout (DO). Finally, we utilized rank-based stochastic pooling (RSP) to substitute the traditional max pooling. This resulted in BDR-CNN, which is a combination of CNN, BN, DO, and RSP. This BDR-CNN was hybridized with a two-layer GCN, and yielded our BDR-CNN-GCN model which was then utilized for analysis of breast mammograms as a 14-way data augmentation method.

Results

As proof of concept, we ran our BDR-CNN-GCN algorithm 10 times on the breast mini-MIAS dataset (containing 322 mammographic images), achieving a sensitivity of 96.20±2.90%, a specificity of 96.00±2.31% and an accuracy of 96.10±1.60%.

Conclusion

Our BDR-CNN-GCN showed improved performance compared to five proposed neural network models and 15 state-of-the-art breast cancer detection approaches, proving to be an effective method for data augmentation and improved detection of malignant breast masses.

中文翻译：

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结合图卷积网络和卷积神经网络改进乳腺癌分类

目标

在一项旨在改善对乳房X线照片中的恶性病变的检测的初步研究中，我们旨在开发一种称为BDR-CNN-GCN的新方法，该方法结合了两个高级神经网络：（i）图卷积网络（GCN）；（ii）卷积神经网络（CNN）。

方法

我们使用了标准的8层CNN，然后集成了两种改进技术：（i）批处理规范化（BN）和（ii）退出（DO）。最后，我们利用基于等级的随机池（RSP）代替了传统的最大池。这导致了BDR-CNN，它是CNN，BN，DO和RSP的组合。将此BDR-CNN与两层GCN杂交，得到我们的BDR-CNN-GCN模型，然后将其作为14方向数据增强方法用于乳房X线照片的分析。

结果

作为概念验证，我们在乳腺mini-MIAS数据集（包含322个乳腺X线照片）上运行了10次BDR-CNN-GCN算法，灵敏度为96.20±2.90％，特异性为96.00±2.31％，准确度为96.10±1.60％。

结论

与五种建议的神经网络模型和15种最新的乳腺癌检测方法相比，我们的BDR-CNN-GCN表现出更高的性能，被证明是一种有效的数据增强方法和改进的恶性乳腺肿块检测方法。