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Deep neural network based lung cancer recognition system

A technology of deep neural network and recognition system, applied in biological neural network models, neural learning methods, character and pattern recognition, etc., can solve problems such as error-prone, cancer cell search and diagnosis workload, and achieve lower quality requirements, The effect of training time advantage and accuracy advantage

Inactive Publication Date: 2017-12-01
厦门市厦之医生物科技有限公司
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Problems solved by technology

However, the cells contained in each histopathological picture are tens of billions of cells, and the search and diagnosis of cancer cells only by manual work is heavy and error-prone

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  • Deep neural network based lung cancer recognition system
  • Deep neural network based lung cancer recognition system
  • Deep neural network based lung cancer recognition system

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Embodiment Construction

[0024] To further illustrate the various embodiments, the present invention is provided with accompanying drawings. These drawings are a part of the disclosure of the present invention, which are mainly used to illustrate the embodiments, and can be combined with related descriptions in the specification to explain the operating principles of the embodiments. With reference to these contents, those skilled in the art should understand other possible implementations and advantages of the present invention. Components in the figures are not drawn to scale, and similar component symbols are generally used to denote similar components.

[0025] The present invention will be further described in conjunction with the accompanying drawings and specific embodiments.

[0026] Such as figure 1 As shown, a lung cancer identification system based on a deep neural network provided by this implementation includes a user service system and a neural network training system, and the user ser...

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Abstract

The invention provides a deep neural network based lung cancer recognition system, and relates to the technical field of medical image processing. The deep neural network based lung cancer recognition system includes a user service system and a neural network training system; the user service system and the neural network training system are isolated from each other through a gateway; the neural network training system includes an application server, a management system, a GPU cluster, and a medical image database; and the user service system is used for providing a service interface for a user, and includes a web page, a service logical processing module, and a database. The complexity of image processing and artificial characteristic extraction of the conventional methods is lowered through automatic learning characteristics of deep learning, the quality demand of a dataset is lowered, discovery and early treatment of the patient buy time, help is provided for diagnosis of a doctor, wrong diagnosis due to human negligence can be avoided, and the accuracy and the training time can be optimized.

Description

technical field [0001] The invention relates to the technical field of medical image processing, in particular to a lung cancer identification system based on a deep neural network. Background technique [0002] Lung cancer is one of the most serious malignant tumors threatening human health and life. Studies have found that if early detection and treatment can be performed, the 5-year survival rate of lung cancer patients will increase by nearly 50%. Analysis of histopathological images is the gold standard for lung cancer diagnosis. However, each histopathological picture contains tens of billions of cells, and the search and diagnosis of cancer cells only by manual work is heavy and error-prone. Therefore, the automatic detection and analysis of histopathological images is a very popular research direction at present. There are currently some methods that apply traditional machine learning, such as the SVM method, to detect and classify cancer cells. The accuracy of th...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F19/00G06K9/62G06N3/08
CPCG06N3/084G06F18/214
Inventor 陈星强
Owner 厦门市厦之医生物科技有限公司
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