Automatic medical image processing system

Abstract

The invention discloses an automatic medical image processing system. Through close combination of image data mining theoretical research and practical application, the mainstream technology of current medical image data mining is discussed. The method is based on improvement of a statistical learning theory, a decision tree classification algorithm, an association rule algorithm and a rough set decision tree algorithm. According to the association rule classification method, the problems existing in association rule classification are solved, formats of multi-source medical equipment data arepackaged, a unified processing interface is adopted to form a normalized data directory, redundant features are removed, attribute reduction is carried out, the classification efficiency is improved,and the processing time is shortened. According to the construction of the medical knowledge graph, a key technology can be applied to computer-aided diagnosis, and a medical image automatic processing system based on an image data mining technology is constructed.

Description

technical field [0001] The invention relates to the technical fields of image recognition and knowledge graph, in particular to an automatic medical image processing system. Background technique [0002] The data mining of medical images aims to mine effective models, associations, rules, changes, irregularities and general laws from massive image data, so as to speed up the process of doctors' decision-making and diagnosis and improve the accuracy of their decision-making and diagnosis. With the rapid development of medical digital equipment, medical information databases include not only structured information of patients, but also a large number of unstructured medical image information of patients, which provides a rich data source for data mining of medical images. [0003] As a method of deep retrieval and mining, knowledge graph is widely used in the accurate acquisition of big data information. In recent years, the exploration of AI diagnosis and treatment based on ...

AI Technical Summary

Problems solved by technology

These systems have difficulties in acquiring and expressing knowledge, and have a certain degree of subjectivity, and their intelligence and robustness are relatively poor.

Moreover, most of the existing medical knowledge graphs are constructed based on text-based unstructured cases and documents, rather than based on medical images, and image data cannot be mined and utilized.

Commonly used image segmentation technologies usually have problems such as difficult classification, poor correlation and contrast, and insufficient granularity of image recognition, so they are not suitable for medical image processing.

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