Tumor recognition system based on artificial intelligence

Abstract

The invention provides a tumor recognition system based on artificial intelligence. The tumor recognition system comprises an image acquisition module, an image library, an image preprocessing module,an image feature extraction module and a tumor recognition module. The tumor recognition method comprises the steps that S1, noise reduction processing is conducted on a CT image of a human organ tobe detected, and then an image feature extraction module extracts image texture features; S2, a focus recognition BP neural network recognizes whether the human organ CT image contains a suspected focus area according to the image texture features; S3, an image preprocessing module performs image segmentation on the human organ CT image containing the suspected lesion area to obtain an area wherea suspected lesion is located; S4, after the region where the suspected focus is located is enhanced, texture features and shape features are further extracted; and S5, the focus type is identified bythe tumor identification BP neural network, and finally benign and malignant identification of the tumor is realized. Compared with the prior art, the method has the advantages of high recognition rate and high recognition accuracy.

Description

technical field [0001] The invention belongs to the technical field of medical image processing, in particular to an artificial intelligence-based tumor recognition system. Background technique [0002] Tumors are divided into benign tumors and malignant tumors. As one of the diseases with the highest morbidity and mortality in the world, malignant tumors seriously threaten people's health and lives. At present, impact examination has become one of the most direct and effective ways to identify tumors. The clinical manifestation of tumors on CT images is that there are several tissue areas of different sizes and irregular shapes in the organ parenchyma, and the boundaries of these areas are relatively vague. . Because cancer patients have no obvious symptoms or atypical imaging findings in the early stage, it is difficult to be discovered and diagnosed. As a result, most of them are in the middle and advanced stages when they are diagnosed, and the best time for treatment i...

AI Technical Summary

Problems solved by technology

[0004] However, due to the complexity of CT image features, it is difficult to directly use the global features of CT images for image recognition, the training speed is slow, the workload is increased, and the recognition accuracy may be low; in addition, after image segmentation For identification, the features at the boundary of the image contour are ignored, which reduces the reliability of tumor identification

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