Ultrasonic thyroid nodule benign and malignant prediction method based on deep learning

A technology for thyroid nodules and prediction methods, which is applied in neural learning methods, image data processing, image enhancement and other directions, can solve the problems of unsatisfactory effect and low accuracy of computer-aided diagnosis, and achieves the effect of solving unsatisfactory effect.

Pending Publication Date: 2021-09-03
江苏乾君坤君智能网络科技有限公司
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Problems solved by technology

[0004] In order to solve the above problems, the purpose of the present invention is to provide a method for predicting benign and malignant ultrasound thyroid nodules ba

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  • Ultrasonic thyroid nodule benign and malignant prediction method based on deep learning
  • Ultrasonic thyroid nodule benign and malignant prediction method based on deep learning
  • Ultrasonic thyroid nodule benign and malignant prediction method based on deep learning

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Embodiment Construction

[0013] In order to make the object, technical scheme and advantages of the present invention clearer, the following in conjunction with the attached figure 1 , the present invention is further described in detail.

[0014] This embodiment provides a method for predicting benign and malignant ultrasound thyroid nodules based on deep learning, such as figure 1 As shown, it includes the following steps: S1: collect ultrasound nodule image samples, and preprocess the ultrasound nodule image samples; the preprocessing includes image sample quality improvement and image sample annotation data; S2: training depth based on image sample annotation data Neural network; S3: Apply the trained deep neural network to the real-time ultrasound image to obtain the segmented nodule area image; S4: Based on the segmented nodule area image through the nodule judgment database, mark the nodule type as The training sample of the nodule prediction model; S5: adopt the feed-forward neural network, u...

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Abstract

The invention particularly relates to an ultrasonic thyroid nodule benign and malignant prediction method based on deep learning. Comprising the following steps: S1, collecting an ultrasonic nodule image sample, and preprocessing the ultrasonic nodule image sample, wherein the preprocessing comprises image sample quality improvement and image sample data labeling; s2, training a deep neural network based on the image sample annotation data; s3, applying the trained deep neural network to a real-time ultrasonic image to obtain a segmented nodule region image; s4, based on the segmented nodule region image, marking nodule types through a nodule judgment database, and taking the nodule types as training samples of a nodule prediction model; s5, adopting a feedforward neural network, using labeled sample data as a training set network, calculating accuracy and recall rate through feature selection, carrying out learning training through repeated iteration, and selecting a network model with the best evaluation result for output; and S6, predicting the type of the thyroid nodule based on the trained network model.

Description

technical field [0001] The present invention relates to the field of smart medical care, in particular to a method for predicting benign and malignant ultrasonic thyroid nodules based on deep learning. Background technique [0002] During the clinical treatment of thyroid nodules, the distinction between benign and malignant thyroid nodules is the basis for the diagnosis and treatment of thyroid nodules. At present, puncture examination and pathological examination are the main means to differentiate benign and malignant thyroid nodules. However, puncture examination and pathological examination have damage. Sex, causing damage to the normal thyroid tissue of the patient. Therefore, how to effectively use the results of non-invasive ultrasonography to predict benign and malignant thyroid nodules is of great significance for the diagnosis and treatment of thyroid nodules. [0003] In order to help doctors improve the accuracy of clinical diagnosis, avoid unnecessary examinat...

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Application Information

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IPC IPC(8): G06T7/00G06T7/11G06K9/62G06N3/04G06N3/08
CPCG06T7/0012G06T7/11G06N3/08G06T2207/20081G06T2207/20084G06T2207/30096G06T2207/10132G06N3/044G06N3/045G06F18/2415
Inventor 张乾君朱建新
Owner 江苏乾君坤君智能网络科技有限公司
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