Medical image classification processing system based on artificial intelligence

Abstract

The invention discloses a medical image classification processing system based on artificial intelligence, and mainly relates to the field of medical image classification. The system comprises: a database, wherein the database is configured in a PACS server and used for obtaining and storing sample image data and inspection image data, the inspection image data is obtained through radiography of an image acquisition terminal, and the sample image data is label information which has a diagnosis result and manually marks medical record information; and a server configured in a background serverand used for realizing data interaction with the database and data interaction with a client, wherein the client is configured in a terminal computer. According to the method, rich image data are fully utilized through a unified format and manual marking, and data expansion and sharing are realized through cloud on a database, so that an efficient and accurate medical picture classification modelcan be constructed.

Description

technical field [0001] The invention relates to the field of medical image classification, in particular to an artificial intelligence-based medical image classification processing system. Background technique [0002] With the rapid development of modern medical imaging technology, medical imaging has become an important auxiliary diagnosis and treatment technology. However, with the integration of various medical imaging equipment such as CT, MR, DSA, DR, and a large number of computer technologies into the diagnosis, the effective use of medical imaging resources has become a very urgent problem. Hospitals generate a large number of medical images every day. If the image categories can be automatically marked, the workload of doctors can be greatly reduced, and the efficiency of medical images can also be improved. Medical image classification has become a very urgent need. [0003] Most of the traditional content-based image classification methods are based on the glob...

AI Technical Summary

Problems solved by technology

[0006] First, accessing large, standardizedly formatted datasets with accurate results is a major challenge, although large clinical datasets from many institutions around the world are openly available, in a tractable and computational form, and accurate enough for learning tasks. The amount of data on the clinical diagnosis results of the

From a global perspective, although the amount of data is huge, it is difficult to use it effectively

[0007] Secondly, with the existing equipment, the data volume of the radiographing results is huge, and the requirements for computer hardware are too high, which cannot be afforded by a single institution. At the same time, various institutions and hospitals are trying to establish their own models and systems, but there are regional barriers. Able to achieve effective sharing of medical resources, hindering the accuracy of deep learning