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首页> 外文期刊>Journal of supercomputing >A new deep convolutional neural network model for classifying breast cancer histopathological images and the hyperparameter optimisation of the proposed model
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A new deep convolutional neural network model for classifying breast cancer histopathological images and the hyperparameter optimisation of the proposed model

机译：一种用于分类乳腺癌组织病理学图像的新深度卷积神经网络模型及拟议模型的封锁率优化

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摘要

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.
机译：除了在多种领域的改进之类的贡献之外，深入学习算法已经产生了显着的结果，这些结果是医学诊断和图像分析，例如药物发现，时间序列建模和优化方法的贡献。关于分析组织病理学乳腺癌图像的分析，这些图像的相似性和不同区域中的健康和毒性组织的存在使整个幻灯片图像上的肿瘤的检测和分类复杂化。在短时间内准确的诊断是需要在乳腺癌中完全治疗。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，并更新模型参数以实现更快的反向化学习。为了在短时间内用较少的硬件训练模型，我们使用了并行计算架构与支持CUDA的图形处理单元。结果表明，深度卷积神经网络模型是一种高性能高达99.05％的有效分类模型。

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