Breast cancer has the second highest frequency of death rate among women worldwide. Early-stage prevention becomes complex due to reasons unknown. However, some typical signatures like masses and micro-calcifications upon investigating mammograms can help diagnose women better. Manual diagnosis is a hard task the radiologists carry out frequently. For their assistance, many computer-aided diagnosis (CADx) approaches have been developed. To improve upon the state of the art, we proposed a deep ensemble transfer learning and neural network classifier for automatic feature extraction and classification. In computer-assisted mammography, deep learning-based architectures are generally not trained on mammogram images directly. Instead, the images are pre-processed beforehand, and then they are adopted to be given as input to the ensemble model proposed. The robust features extracted from the ensemble model are optimized into a feature vector which are further classified using the neural network (nntraintool). The network was trained and tested to separate out benign and malignant tumors, thus achieving an accuracy of 0.88 with an area under curve (AUC) of 0.88. The attained results show that the proposed methodology is a promising and robust CADx system for breast cancer classification. Graphical Abstract Flow diagram of the proposed approach. Figure depicts the deep ensemble extracting the robust features with the final classification using neural networks.

中文翻译：

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基于深度特征的乳房X线照片自动分类。

乳腺癌是全世界女性中第二高的死亡率。由于未知原因，早期预防变得复杂。但是，一些典型的特征如乳房X线照片上的肿块和微钙化可以帮助更好地诊断女性。手动诊断是放射科医生经常执行的一项艰巨任务。为了帮助他们，已经开发了许多计算机辅助诊断（CADx）方法。为了改进现有技术，我们提出了一种用于自动特征提取和分类的深度集成转移学习和神经网络分类器。在计算机辅助的乳腺X线摄影中，通常不直接在乳腺X线照片上训练基于深度学习的架构。取而代之的是，对图像进行预先预处理，然后采用它们作为所提出的集成模型的输入。从集成模型中提取的鲁棒特征被优化为特征向量，然后使用神经网络（nntraintool）对其进行分类。对该网络进行了培训和测试，以分离出良性和恶性肿瘤，从而达到0.88的准确度，曲线下面积（AUC）为0.88。所获得的结果表明，所提出的方法是用于乳腺癌分类的有希望且鲁棒的CADx系统。拟议方法的图形抽象流程图。该图描绘了深度集成使用神经网络在最终分类中提取鲁棒特征。因此，曲线下面积（AUC）为0.88，精度为0.88。所获得的结果表明，所提出的方法是用于乳腺癌分类的有希望且鲁棒的CADx系统。拟议方法的图形抽象流程图。该图描绘了深度集成使用神经网络在最终分类中提取鲁棒特征。因此，曲线下面积（AUC）为0.88，精度为0.88。所获得的结果表明，所提出的方法是用于乳腺癌分类的有希望且鲁棒的CADx系统。拟议方法的图形抽象流程图。该图描绘了深度合奏，使用神经网络通过最终分类来提取鲁棒特征。