Breast cancer prevention self-service health cloud service system based on deep convolutional neural network

Abstract

The invention discloses a breast cancer prevention self-service health cloud service system based on a deep convolutional neural network. The self-service health cloud service system mainly comprises a convolutional neural network applied to deep learning and training recognition, a partitioning module which is used for partitioning a breast area from a mammary gland molybdenum target radiography image based on a full-convolutional neural network, the deep convolutional neural network which is applied to BI-RADS classification and evaluation, and a self-service health cloud service platform which is applied to early preventing and treating breast cancer according to a recognized mammary gland inner structure, tumor and calcification types. The self-service health cloud service system can effectively improve the automatic and intelligent level of breast cancer screening based on a mobile internet, enable more women to know and participate in self-service health detection, evaluation and guidance, further improve the health consciousness of the public, and improve self health management capacity.

Description

technical field [0001] The present invention relates to the application of technologies such as medical image diagnosis, mobile Internet, database management, computer vision, image processing, pattern recognition, deep neural network and deep learning in the field of self-service health care, especially relates to a deep convolutional neural network-based Self-service health cloud service system for early detection and early diagnosis of breast cancer. Background technique [0002] In recent years, the incidence of breast cancer in our country has been increasing year by year, especially in some big cities, such as Shanghai, Beijing and other places, breast cancer has jumped to the first place in the incidence of malignant tumors in women. [0003] Screening is an important method for early detection of breast cancer. High-quality mammography (ie, mammography) combined with clinical outpatient and breast ultrasonography is currently the most important screening method. Mam...

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Problems solved by technology

[0030] To sum up, there are still several thorny problems in the early diagnosis of breast cancer using convolutional neural networks based on deep learning: 1) how to accurately segment the overall image of the breast from the complex background; 2) how to Use as little labeled breast cancer image data as possible to accurately obtain various characteristic data of breast cancer; 3) How to build a highly automated self-servi

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