Deep learning algorithms have yielded remarkable results in medical diagnosis and image analysis, besides their contribution to improvements in a number of fields such as drug discovery, time-series modelling and optimisation methods. With regard to the analysis of histopathologic breast cancer images, the similarity of those images and the presence of healthy and tumourous tissues in different areas complicate the detection and classification of tumours on whole slide images. An accurate diagnosis in a short time is a need for full treatment in breast cancer. A successful classification on breast cancer histopathological images will overcome the burden on the pathologist and reduce the subjectivity of diagnosis. In this study, we propose a deep convolutional neural network model. The model uses various algorithms (i.e., stochastic gradient descent, Nesterov accelerated gradient, adaptive gradient, RMSprop, AdaDelta and Adam) to compute the initial weight of the network and update the model parameters for faster backpropagation learning. In order to train the model with less hardware in a short time, we used the parallel computing architecture with Cuda-enabled graphics processing unit. The results indicate that the deep convolutional neural network model is an effective classification model with a high performance up to 99.05% accuracy value.

中文翻译：

---

一种新的用于分类乳腺癌组织病理学图像的深度卷积神经网络模型和所提出模型的超参数优化

深度学习算法除了在药物发现、时间序列建模和优化方法等许多领域的改进方面做出了贡献之外，还在医学诊断和图像分析方面取得了显著成果。关于组织病理学乳腺癌图像的分析，这些图像的相似性以及不同区域中健康和肿瘤组织的存在使整个幻灯片图像上的肿瘤检测和分类变得复杂。在短时间内准确诊断是乳腺癌全面治疗的需要。对乳腺癌组织病理学图像的成功分类将克服病理学家的负担并降低诊断的主观性。在这项研究中，我们提出了一个深度卷积神经网络模型。该模型使用各种算法（即 随机梯度下降、Nesterov 加速梯度、自适应梯度、RMSprop、AdaDelta 和 Adam）来计算网络的初始权重并更新模型参数以加快反向传播学习。为了在短时间内用更少的硬件训练模型，我们使用了带有 Cuda-enabled 图形处理单元的并行计算架构。结果表明，深度卷积神经网络模型是一种有效的分类模型，具有高达 99.05% 的准确率值。我们将并行计算架构与支持 Cuda 的图形处理单元一起使用。结果表明，深度卷积神经网络模型是一种有效的分类模型，具有高达 99.05% 的准确率值。我们将并行计算架构与支持 Cuda 的图形处理单元一起使用。结果表明，深度卷积神经网络模型是一种有效的分类模型，具有高达 99.05% 的准确率值。