Breast cancer is a common cancer in women. Early detection of breast cancer in particular and cancer, in general, can considerably increase the survival rate of women, and it can be much more effective. This paper mainly focuses on the transfer learning process to detect breast cancer. Modified VGG (MVGG) is proposed and implemented on datasets of 2D and 3D images of mammograms. Experimental results showed that the proposed hybrid transfer learning model (a fusion of MVGG and ImageNet) provides an accuracy of 94.3%. On the other hand, only the proposed MVGG architecture provides an accuracy of 89.8%. So, it is precisely stated that the proposed hybrid pre-trained network outperforms other compared Convolutional Neural Networks. The proposed architecture can be considered as an effective tool for radiologists to decrease the false negative and false positive rates. Therefore, the efficiency of mammography analysis will be improved.

中文翻译：

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基于混合迁移学习的现代乳腺X线摄影诊断乳腺癌

乳腺癌是女性常见的癌症。特别是早期发现乳腺癌和一般癌症，可以大大提高女性的存活率，而且效果会更好。本文主要关注转移学习过程以检测乳腺癌。在乳房 X 线照片的 2D 和 3D 图像数据集上提出并实施了改进的 VGG (MVGG)。实验结果表明，所提出的混合迁移学习模型（MVGG 和 ImageNet 的融合）提供了 94.3% 的准确率。另一方面，只有提出的 MVGG 架构提供了 89.8% 的准确率。因此，准确地说，所提出的混合预训练网络优于其他比较卷积神经网络。所提出的架构可以被认为是放射科医生降低假阴性和假阳性率的有效工具。因此，乳腺X线摄影分析的效率将得到提高。