Breast lump information extraction and classification method for breast X-ray image

Abstract

The invention discloses a breast lump information extraction and classification method for a breast X-ray image. The method comprises the following steps of S1, inputting the breast X-ray image into four parallel convolutional neural networks; S2, extracting high-level semantic features of the breast X-ray image on the basis of the four parallel convolutional neural networks; and S3, performing multi-label multi-task learning network training on the extracted high-level semantic features, and obtaining classification information of breast lumps. According to the breast lump information extraction and classification method for the breast X-ray image, redundant features extracted by the convolutional neural networks can be effectively removed; four classification tasks can be mutually entangled and constrained and mutually promoted through a multi-label multi-task network; clear breast lump classification information is provided for doctors; and auxiliary diagnosis of breast lump relateddiseases is provided.

Description

technical field [0001] The invention belongs to the technical field of mammogram image processing, and in particular relates to a method for extracting and classifying mammary gland lump information from mammogram images. Background technique [0002] Breast cancer is one of the most common malignant tumors in women. According to relevant data, a woman is diagnosed with breast cancer every two minutes. In my country, breast cancer is the main cause of death among women aged 40 to 50, with an average of one woman dying of breast cancer every 12 minutes; among urban women, 3 to 4 of every 10,000 women suffer from breast cancer. Not only that, the incidence of breast cancer is rising sharply at a rate of 3% to 4% per year. Therefore, early detection, early diagnosis and early treatment are recognized as effective measures to control the development of breast cancer, and are of great significance to improve the survival rate and quality of life of patients, among which early det...

AI Technical Summary

Problems solved by technology

[0006] Aiming at the above-mentioned deficiencies in the prior art, the method for extracting and classifying mammary gland mass information from mammogram images provided by the present invention solves the problem of using a convolutional neural network to analyze mammogram images in the prior art, which can only be performed simply. It is difficult to provide doctors with accurate diagnostic evidence for the extraction of benign and malignant tumor information

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