Lightweight neural network-based mammary molybdenum target image deep learning classification method

Abstract

The invention relates to a lightweight neural network-based mammary molybdenum target image deep learning classification method. According to the method, a deep learning-based image classification algorithm is applied; mammary density classification for mammary molybdenum target images is realized; and a lightweight neural network-based deep learning framework is applied. The adaptability in small-scale image data sets is remarkably improved; the accuracy and processing speed of the mammary density classification are improved; and automatic mammary density classification of the mammary molybdenum target images can be realized.

Description

technical field [0001] The invention belongs to the field of biomedicine, and in particular relates to a deep learning classification method for mammography images based on a lightweight neural network. Background technique [0002] The full name of mammography mammography mammography, also known as molybdenum palladium examination, is the first choice, the simplest and most reliable non-invasive detection method for the diagnosis of breast diseases. It is relatively painless, easy to implement, and has high resolution. , good reproducibility, the retained images can be compared before and after, and are not limited by age and body shape. It has been used as a routine inspection method at present. As a relatively non-invasive examination method, mammography can comprehensively and correctly reflect the general anatomical structure of the whole breast, observe the influence of various physiological factors such as menstrual cycle, pregnancy, breast-feeding, etc. on the struct...

AI Technical Summary

Problems solved by technology

However, due to the small number of samples, large differences, and uneven density distribution of medical mammography images, for the application of mammography image processing and analysis, manual recognition can only simply divide the boundaries of breast regions and determine the density of breasts in the region. Qualitative estimation has been difficult to meet the accuracy and speed requirements of breast density classification, and the traditional automatic density classification method of mammography images also has shortcomings that seriously affect the analysis results: the breast itself and its shape are different, it is difficult to use the traditional The method based on the morphological model segments various tissues, resulting in inaccurate boundary division between the mammary gland and the image background; the density distribution of various tissues inside the mammary gland is extremely uneven, which makes the statistical results of the density distribution histogram easy to be partial and complete, resulting in mammary gland An error occurred in the statistical analysis of the overall density, which seriously affected the discrimination accuracy and processing speed of mammography density classification

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