Thyroid ultrasound image nodule automatic diagnosis system based on multi-scale convolutional neural network

Abstract

The invention provides a thyroid ultrasound image nodule automatic diagnosis system based on a multi-scale convolutional neural network. The system includes a thyroid nodule coarse-to-fine classification module, a thyroid nodule region automatic detection module, and a thyroid nodule fine classification module. The size features of different sensing regions are extracted through a multi-scale feature fusion convolutional neural network, and then, the context semantic features of a thyroid nodule can be extracted according to local and global information, and the thyroid nodule can be automatically located. Through multi-scale coarse-to-fine feature extraction based on a neural network and the design of a multi-scale fine classification AlexNet of a pyramid structure, the position of a focus and the probability that the focus is benign or malignant can be accurately predicted, doctors can be assisted in diagnosing a thyroid focus, and the objectivity of diagnosis is improved. The systemhas the characteristics of good real-time performance and high accuracy.

Description

technical field [0001] The invention relates to an automatic diagnosis system for thyroid ultrasound image nodules based on a multi-scale convolutional neural network, which belongs to the field of deep learning and auxiliary medical treatment. Background technique [0002] In the 1940s, ultrasound imaging technology began to be used clinically, and in 1962, it began to diagnose the thyroid gland. Thyroid ultrasound imaging technology can provide doctors with information about the tissue of the thyroid gland, and has become the preferred technical diagnostic method for modern doctors. [0003] Convolutional neural network technology has had a research history of more than 30 years since the 1980s. Convolutional neural network was first applied to handwritten digit recognition. In the field of computer vision, recognition of faces and objects has always been A very challenging research hotspot. In recent years, with the development of the Internet, the acquisition of a larg...

AI Technical Summary

Problems solved by technology

However, at present, due to the characteristics of low contrast, inconspicuous borders, and small visual differences in ultrasound images, the research on ultrasound images of the thyroid still faces many challenges, because there are more complex physical and visual feature mechanisms behind these phenomena. Convincing simulation effects require the support of multi-disciplinary interdisciplinary theory and efficient algorithm design combining software and hardware

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