Kidney disease automatic detection method and system based on machine learning

Abstract

The invention discloses a kidney disease automatic detection method and system based on machine learning, and the system comprises a kidney image collection module, a kidney image management module, a kidney image intelligent diagnosis module and a kidney disease classification module. Kidney images corresponding to the kidney disease types are stored and updated in the kidney image management module, the kidney disease types are inferred through the kidney image intelligent diagnosis module by utilizing a machine learning algorithm, and the kidney disease classification module classifies the diagnosed kidney diseases according to different kidney disease types. Therefore, efficient and accurate diagnosis of various kidney disease types is realized, and the kit has great significance in diagnosis and early screening of kidney diseases.

Description

technical field [0001] The present disclosure relates to systems and methods for image analysis and medical diagnostic testing using image analysis, and more particularly, the present disclosure relates to a method and system for automatic detection of kidney disease based on machine learning. Background technique [0002] Medical imaging refers to the technology and process of obtaining internal images of the human body or a certain part of the human body in a non-invasive manner for medical treatment or medical research. With the rapid development of various science and technology, medical image processing technology has also made rapid progress, but with the development of science and technology and the promotion of medical image applications, more and more medical images need to be interpreted by doctors, and medical image interpretation is gradually become a challenging job. The traditional medical image interpretation and diagnosis process mainly relies on experienced...

AI Technical Summary

Problems solved by technology

The interpretation and diagnosis of traditional medical images mainly rely on experienced doctors. This process is subjective. In addition, in the process of manual medical image interpretation, interpretation errors may occur due to cognitive limitations or fatigue of doctors. Misdiagnosis, these defects have shown the importance of using effective medical image analysis technology to improve the accuracy of disease diagnosis results

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