Pulmonary nodule detection method and device based on neural network, and image processing equipment

Abstract

The invention provides a pulmonary nodule detection method and device based on a neural network and image processing equipment. The method comprises the steps of S1, carrying out the recognition and segmentation of a pulmonary nodule in a CT image based on a 3D DCNN network frame, and obtaining candidate pulmonary nodule data, S2, constructing eigenvalue vectors of candidate pulmonary nodule data in the CT image to obtain a sample data set, and S3, dividing the sample data set into a training set and a test set according to a proportion, performing classification training on the training set by using a pre-constructed GRU model, inputting the test set into the trained GRU model for detection, and outputting a pulmonary nodule detection result. According to the method, after the CT lung image is automatically detected and segmented, the focus area is extracted, the feature value is constructed, the neural network is used for modeling, the candidate pulmonary nodule area is detected, false positive is reduced, and the accuracy of automatic detection of the lung image is improved.

Description

technical field [0001] The present disclosure relates to the technical field of medical imaging, in particular to a neural network-based pulmonary nodule detection method, device and image processing equipment. Background technique [0002] Lung cancer is a common fatal malignant tumor with high morbidity and mortality in China. Most lung cancer patients are already in the middle and advanced stages after being discovered, which brings great problems to the treatment effect. Therefore, the early detection and diagnosis of lung cancer is of great significance to patients, which can improve the effect of treatment and prognosis. The traditional diagnostic mode is that doctors analyze and judge pulmonary nodules through CT images. However, since the patient's CT image includes 200-300 images at the image level, manually finding and analyzing pulmonary nodules is not only time-consuming and laborious, but also may cause missed diagnosis due to factors such as fatigue. Through...

AI Technical Summary

Problems solved by technology

However, in this way, there are more false positives for pulmonary nodules, and the places where the edges are not fine enough to be optimized

More false positives increase the time for doctors to judge pulmonary nodules, which affects the accuracy of doctors' judgments

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