Breast cancer cell characteristic analysis system based on deep learning

Abstract

The invention discloses a breast cancer cell characteristic analysis system based on deep learning. Based on the deep learning, the system constructs a multilevel convolutional neural network to extract multilevel features, so higher analysis accuracy can be realized; an activation function of a model in the breast cancer cell characteristic analysis system disclosed by the invention is an unsaturated ReLU function, which has faster convergence properties; a pooling layer in the breast cancer cell characteristic analysis system disclosed by the invention adopts an overlapped pooling operation, it can be proved by cross validation that, compared with a traditional non-overlapped pooling layer, the overlapped pooling can further improve the analysis accuracy; and the breast cancer cell characteristic analysis system disclosed by the invention adopts a training mode of sparse autocoder pre-training and Dropout fine tuning to effectively reduce the over fitting of the model and enhance the generalization ability of the trained model, so that the analysis accuracy can be further improved.

Description

technical field [0001] The present invention relates to the technical field of cell feature analysis, in particular to a breast cancer cell feature analysis system based on deep learning. Background technique [0002] Deep learning is a hot technology in machine learning at present. The concept originated from the research of artificial neural network. Its core idea is to use unsupervised layer-by-layer pre-training, which effectively prevents the problem of gradient dispersion, so that when the neural network has more layers Effective training is also possible. And more layers mean that the network can express more complex functions and learn more advanced features. Thus achieving better recognition performance. [0003] Its essence is to build an architectural model with multiple hidden layers, and to train through large-scale data to obtain a large amount of more representative feature information, so as to classify and predict samples and improve the accuracy of classi...

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Problems solved by technology

[0004] In the prior art, there is no relevant scheme for analyzing the characteristics of breast cancer cells. Therefore

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