A diagnosis method of thyroid nodules based on deep learning network

Abstract

The invention discloses a method for diagnosing thyroid nodules based on a deep learning network, which belongs to the field of image processing and artificial intelligence-assisted diagnosis of diseases, and includes the following steps: collecting original ultrasonic images and pathological reports of thyroid nodules in thyroid patients, and constructing thyroid nodules section database; ultrasound image preprocessing; based on Xception-JFT's Deeplab v3+ method, perform semantic segmentation on the ultrasound image preprocessed in step 2 to form a semantic segmentation result map; based on deep learning network to distinguish between benign and malignant thyroid nodules; form Thyroid nodule diagnostic information report. The present invention adopts the Deeplab v3+ algorithm based on Xeption-JFT to establish a thyroid ultrasound image segmentation network model, achieves the optimal segmentation effect by continuously improving the backbone network Xception, and can automatically and quickly identify nodule information with high accuracy and high robustness , automatically extract image features for accurate segmentation to obtain better diagnostic results, and provide an objective reference for clinical diagnosis.

Description

technical field [0001] The invention belongs to the field of image processing and artificial intelligence-assisted diagnosis of diseases, and in particular relates to a method for diagnosing thyroid nodules based on a deep learning network. Background technique [0002] Thyroid nodules are lumps in the thyroid gland. The causes of the disease are complex and the number of patients is large. According to the survey, 68% of the patients in the country are potential patients with thyroid nodules. At the same time, the phenomenon of overdiagnosis and treatment is serious, and it has gradually become one of the diseases that threaten human health. [0003] Currently, nodules can be diagnosed by physician palpation, needle biopsy, and ultrasound images. Ultrasound has become the first choice for diagnosis because of its high sensitivity, non-invasive, non-radiation, and real-time characteristics. For thyroid nodules that cannot be characterized by ultrasound, invasive FNA (fine-n...

AI Technical Summary

Problems solved by technology

Ultrasound has become the first choice for diagnosis because of its high sensitivity, non-invasive, non-radiation, and real-time characteristics. For thyroid nodules that cannot be characterized by ultrasound, invasive FNA (fine-needle aspiration biopsy) should be used to confirm. However, the uncertainty rate of FNA It is also as high as 30%, and most doctors often choose conservative treatment for unnecessary surgical resection, causing patients to take drugs for life, causing irreparable harm to patients

[0004] According to statistics, the rate of misdiagnosis and missed diagnosis of thyroid nodules by ultrasound is as high as 34%. In addition, due to the huge outpatient volume of thyroid diseases, each ultrasound doctor needs to diagnose 120-150 patients per day on average, and the whole process of ultrasound diagnosis lasts for 20 minutes. It takes about 10 hours a day to focus on film reading, which is a huge workload

In addition, there is currently a shortage of 200,000 doctors specializing in ultrasound in the country, and the shortage of doctor resources

[0005] Due to the large amount of noise in ultrasound images and the significant differences in the size and location of thyroid glands in different populations, traditional image processing methods are often difficult to obtain better results.

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