Chest X-ray disease detection device and method based on dual-channel separation network

Abstract

The invention discloses a chest X-ray disease detection device and method based on a dual-channel separation network, and the device comprises a processor which is configured to carry out the following operations: carrying out the preprocessing of a training data set of a chest X-ray image, and dividing the training data set into a data enhancement part and a normalization regularization part; training a dual-channel separation depth network, extracting and fusing features of each level in different channels, minimizing a loss function in a classification layer, and performing complete training of the network; classifying the input chest X-ray images by using the trained network to obtain lesion types and probabilities contained in the images; and using the trained network to locate the disease in the input chest X-ray image. The method can greatly improve the recognition accuracy of chest lesions in a chest disease recognition task, and can achieve the precise positioning of a diseaseposition in a visualization task.

Description

technical field [0001] The invention relates to the fields of computer vision and medical image processing, in particular to a chest X-ray disease detection device and method based on a dual-channel separation network. Background technique [0002] Medical X-ray images are an imaging method that reflects the internal structure of the human body according to the different absorption levels of X-rays by different tissues, and can display a variety of complex pathological features. Chest X-ray (CXR) imaging is one of the most widely available radiological examinations for screening and clinical diagnosis. However, automatic detection and understanding of CXR images has become a technically challenging task at present due to the complex pathology of different types of lung lesions on the images. Because chest X-ray imaging technology is not only fast, simple, but also economical, people usually choose X-ray when checking chest diseases such as thorax (including ribs, thoracic s...

AI Technical Summary

Problems solved by technology

[0005] 1) Lack of training data sets and related annotations/labels of images, professional knowledge limitations lead to high difficulty in labeling

In addition, even if there is data labeled by experts, the existing label noise will also interfere

[0006] 2) The visual patterns extracted from different types of chest disease samples are usually highly diverse in appearance, size, and location, and traditional image processing methods cannot learn meaningful representations du

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