A breast mass image recognition method and device

Abstract

The invention provides a mammary gland mass image recognition method and device. The method comprises the steps that a mammary gland magnetic resonance image to be recognized is acquired; Inputting the mammary gland magnetic resonance image to be identified into a constructed mammary gland mass identification model to obtain a mass identification result in the mammary gland magnetic resonance image to be identified; Wherein the lump recognition model adopts a deep full convolutional neural network model; wherein the coding process of the deep full convolutional neural network model adopts a basic convolutional module in the U-shaped convolutional neural network model to perform feature extraction, and the decoding process of the deep full convolutional neural network model adopts dense connection to perform size unification on to-be-fused feature maps and then perform fusion. According to the embodiment of the invention, automatic recognition of the breast lumps is realized, and the recognition accuracy of the breast lumps is improved.

Description

technical field [0001] This specification belongs to the technical field of image processing, and in particular relates to a breast mass image recognition method and device. Background technique [0002] Breast cancer is the cancer with the highest incidence rate in women. The cure rate of early breast cancer is much higher than that of advanced breast cancer. Early detection, early diagnosis and early treatment are the key to reducing the mortality rate of breast cancer. Imaging examinations, including mammography, ultrasound, and magnetic resonance imaging, are common techniques for early breast cancer screening. Breast cancer screening can be performed by using the results of imaging examinations to identify breast masses. The size and shape of breast masses in different patients vary greatly, which brings great challenges to automatic segmentation methods, and mass segmentation is the first priority for all subsequent breast cancer diagnosis and treatment. Steps, so the...

AI Technical Summary

Problems solved by technology

Manually extracting features depends on the professional and empirical knowledge of researchers, which often has certain limitations and subjectivity, and the results will be greatly affected

The automatic segmentation method of breast mass in the prior art usually only gives the position of the mass, and does not have the shape and size information of the mass

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