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Construction method of intelligent identification missing prevention system for breast ultrasound image lesions

An ultrasonic image and intelligent recognition technology, applied in image analysis, image data processing, instruments, etc., can solve problems such as delaying patient treatment timing and high missed diagnosis rate, and achieve the effects of reducing the cost of viewing films, robust and stable models, and improving efficiency

Inactive Publication Date: 2021-04-20
SICHUAN UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

This method may have a very high rate of missed diagnosis. For some patients who were originally positive, if the diagnosis is negative, it may seriously delay the patient's treatment opportunity.

Method used

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  • Construction method of intelligent identification missing prevention system for breast ultrasound image lesions
  • Construction method of intelligent identification missing prevention system for breast ultrasound image lesions
  • Construction method of intelligent identification missing prevention system for breast ultrasound image lesions

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0041] A method for constructing a system for intelligent identification of lesions in breast ultrasound images and an anti-missing judgment system, such as figure 1 As shown, including S1. Data preparation, S2. Deep neural network model design, S3. Missed diagnosis prevention data consistency design, S4. Missed diagnosis prevention model training, S5. Missed diagnosis prevention model test.

Embodiment 2

[0043] The difference between this embodiment and Embodiment 1 is that the data preparation includes the following steps: S1.1. Data labeling: mark the exact position of the breast cancer lesion in the color Doppler ultrasound image to obtain the label of the corresponding image, each The color ultrasound image corresponds to a label, 0 represents normal breast or benign tumor, and 1 represents malignant tumor.

[0044] S1.2. Data preprocessing: For each color Doppler ultrasound report, information other than the color Doppler ultrasound image is cut out to prevent the interference of extra information. Restretch or shrink each color Doppler image to the same size to fit the network input size.

[0045] S1.3. Data set division: Divide the preprocessed color Doppler ultrasound image data into a training set and a test set at a ratio of 4:1 to evaluate the prediction results of the model and the prediction performance of the test model.

Embodiment 3

[0047] The difference between this embodiment and Embodiment 1 is that the neural network mainly consists of three parts: an input layer, a hidden layer, and an output layer. The hidden layer is composed of multiple neurons connected to each other, and various nonlinear transformations are used to fit the nonlinear relationship between the data, so as to achieve the function of predicting the image category. In the present invention, the hidden layer is composed of multiple layers of neurons to form a deep neural network, so as to learn deeper features to obtain the mapping relationship between input data and its features, making the model more stable and the prediction effect better.

[0048] The design of the deep neural network model includes the following steps: S2.1. Training data enhancement: the color Doppler ultrasound image to be trained will be randomly rotated at an angle, randomly centered, randomly vertically flipped, and randomly horizontally flipped to expand the...

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Abstract

The invention discloses a construction method of a breast ultrasound image focus intelligent identification missed judgment prevention system. The construction method comprises the steps: S1, data preparation, S2, deep neural network model design, S3, missed diagnosis prevention data consistency design, S4, missed diagnosis prevention model training, and S5, missed diagnosis prevention model testing. According to the construction method of the breast ultrasound image focus intelligent identification missed judgment prevention system, the constructed system is additionally provided with the missed diagnosis prevention model module, the missed diagnosis prevention model module is designed by using a generative adversarial neural network, and the missed diagnosis rate of an original computer intelligent diagnosis system can be reduced, so that the effect and the precision of auxiliary diagnosis are improved; and the film viewing cost of doctors in hospitals is reduced and the efficiency is improved.

Description

technical field [0001] The invention relates to the field of ultrasound imaging technology, in particular to a method for constructing a system for intelligently identifying lesions in breast ultrasound images and preventing omissions. Background technique [0002] Breast cancer is a malignant tumor that occurs in the glandular epithelial tissue of the breast. 99% of breast cancers occur in women and only 1% in men. Female mammary glands are composed of skin, fibrous tissue, mammary glands and fat. Mammary glands are not an important organ to maintain human life. Breast cancer in situ is not fatal. However, due to the loss of the characteristics of normal cells in breast cancer cells, the The connection between them is loose and easy to fall off. Once the cancer cells fall off, the free cancer cells can spread throughout the body with the blood or lymph fluid, forming metastasis, which is life-threatening. At present, breast cancer has become a common tumor that threatens...

Claims

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Application Information

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IPC IPC(8): G16H50/20G16H15/00G06T7/00
Inventor 章毅郭泉戚晓峰刘文杰周尧张蕾
Owner SICHUAN UNIV
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