AbstractBreast cancer is a major cause of transience amongst women. In this paper, two novel techniques, ResNet50 and VGG-16, are utilised and re-trained to recognise two classes rather than 1000 classes with high accuracy and low computational requirements. In addition, transfer learning and data augmentation are performed to solve the problem of lack of tagged data. To get a better accuracy, the support vector machine (SVM) classifier is utilised instead of the last fully connected layer. Our models performance are verified utilising k -fold cross-validation. Our proposed techniques are trained and evaluated on three mammographic datasets: mammographic image analysis society, digital database for screening mammography (DDSM) and the curated breast imaging subset of DDSM. This paper explains end-to-end fully convolutional neural networks without any prepossessing or post-processing. The proposed technique of employing ResNet50 hybridised with SVM achieves the best performance, specifically with the DDSM dataset, producing 97.98% accuracy, 98.46% area under the curve, 97.63% sensitivity, 96.51% precision, 95.97% F1 score and computational time 1.8934 s.

中文翻译：

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基于深度学习的新型乳腺癌分类框架

摘要乳腺癌是导致女性暂时性疾病的主要原因。在本文中，使用了两种新颖的技术ResNet50和VGG-16，并对它们进行了重新训练，以识别两类而不是1000类，它们具有很高的准确性和较低的计算要求。另外，执行转移学习和数据扩充以解决缺少标记数据的问题。为了获得更好的精度，使用了支持向量机（SVM）分类器，而不是最后一个完全连接的层。我们的模型性能经过验证ķ 折交叉验证。我们提出的技术在三个乳腺摄影数据集上进行了培训和评估：乳腺摄影图像分析学会，筛查乳腺摄影的数字数据库（DDSM）和DDSM的精选乳腺成像子集。本文解释了没有任何前提或后处理的端到端全卷积神经网络。将ResNet50与SVM混合使用的建议技术达到了最佳性能，特别是在DDSM数据集上，可产生97.98％的精度，曲线下面积的98.46％，97.63％的灵敏度，96.51％的精度，95.97％的F1分数和1.8934 s的计算时间。