Thyroid nodule calcification recognition device based on deep learning

Abstract

The invention relates to a thyroid nodule calcification recognition device based on deep learning. The thyroid nodule calcification recognition device comprises an ultrasonic image acquisition module used for acquiring an ultrasonic image data set; a labeling module, which is used for labeling the thyroid nodule boundary, the calcification region and the calcification type of each ultrasonic image; a nodule-of-interest image extraction module, which is used for intercepting a thyroid nodule boundary in each ultrasonic image to obtain a nodule-of-interest image data set; a convolutional neural network construction module, which is used for constructing a convolutional neural network XDNet-11222; a convolutional neural network training module, which is used for training a convolutional neural network XDNet-11222 through the nodule-of-interest image data set; and a thyroid nodule calcification detection module, which is used for carrying out calcification type detection on the input image through the trained convolutional neural network XDNet-11222. According to the invention, the calcification type of the input thyroid nodule ultrasonic image can be effectively identified.

Description

technical field [0001] The invention relates to the technical field of assisted medical diagnosis, in particular to a device for identifying thyroid nodule calcification based on deep learning. Background technique [0002] Thyroid nodules refer to lumps in the thyroid gland, which are common clinical diseases and can be caused by various etiologies. Clinically, there are many thyroid diseases, such as thyroid degeneration, inflammation, autoimmunity, and new organisms, which can all be manifested as nodules. Thyroid nodules can be single or multiple. The incidence of multiple nodules is higher than that of single nodules, but the incidence of thyroid cancer in single nodules is higher. [0003] As the basis for judging benign and malignant nodules, and even for cancer diagnosis, calcification is one of the important features of thyroid nodules. The existing ultrasonic diagnosis methods all use ultrasound to scan the thyroid gland of the patient to form an ultrasound pictu...

AI Technical Summary

Problems solved by technology

The existing ultrasonic diagnosis methods all use ultrasound to scan the thyroid gland of the patient to form an ultrasound picture of the thyroid gland, and then the doctor manually recognizes and judges the ultrasound picture of the thyroid gland. The disadvantages are: slow diagnosis efficiency; Doctors require a high level of experience, so the number of qualified doctors is limited, resulting in shortage of doctor resources and high costs

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