Chaotic microwave breast tumor imaging classification device and method based on deep learning

Abstract

The invention relates to a chaotic microwave breast tumor imaging classification device and method based on deep learning. According to the method, a plurality of lasers with different wavelengths areinjected into a multi-longitudinal-mode laser to generate a plurality of quasi-orthogonal chaotic ultra-wideband signals, a neural network for a proposed double-classification model is built througha deep learning algorithm, and an image classification effect model is realized. In supervised learning, a data set with existing attributes and marks is provided for a neural network, a training model capable of classifying images is established by initializing weights, performing gradient descent, checking correctness of gradient descent and the like, the input images are judged, and tumor recognition and benign and bad classification are performed by using a deep learning double-classifier model. The chaotic microwave photon radar with excellent characteristics is combined with deep learning, high-precision identification of breast tumors is realized, and the problems of complexity and misjudgment of intuitive tumor judgment of doctors are solved.

Description

technical field [0001] The present invention relates to the technical field of signal processing, and more specifically, to a chaotic microwave breast tumor imaging classification device and method based on deep learning. Background technique [0002] Computed tomography (CT imaging) uses a projection reconstruction algorithm to reconstruct the image of the medical body, which has the advantage of three-dimensional positioning, but the radiation of CT imaging can penetrate cells and induce some cancer cells, and the imaging resolution of this method is low. It is complicated, and the detection cost is high, so it is difficult to become a routine detection method. [0003] Conventional ultrasonography methods include, for example, X-ray mammography, B-ultrasound detection, and color Doppler ultrasonography. X-ray molybdenum imaging is a relatively effective method for breast screening, with high resolution and good repeatability, but this technology uses two-dimensional pers...

AI Technical Summary

Problems solved by technology

[0007] The present invention provides a chaotic microwave breast tumor imaging classification device and method based on deep learning. The present invention provides a chaotic microwave photon radar breast tumor imaging classification method and device based on deep feature learning, which has excellent performance. The characteristic chaotic microwave photon radar is combined with deep learning to realize high-precision identification of breast tumors and solve the cumbersome and misjudgment problems of doctors intuitively judging the existence of tumors

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