Disease prediction system based on block chain and medical image

Abstract

The disease prediction system based on the block chain and the medical image comprises a medical image acquisition module, a medical image preprocessing module, a disease prediction module and an intelligent diagnosis module, the medical image acquisition module is used for acquiring a medical image of a patient, and the medical image preprocessing module is used for preprocessing the medical image in a database; the disease prediction module is used for training a target medical image through a neural network and matching disease features with the highest similarity, and the intelligent diagnosis module is used for completing man-machine interaction service including displaying a prediction result and giving suggestions for preventing disease deterioration. The method has the beneficial effects that the diseases can be effectively predicted, so that the diseases can be found and treated early, and the death rate of the diseases is reduced.

Description

technical field [0001] The invention of the invention relates to the field of medical and health, in particular to a disease prediction system based on blockchain and medical images. Background technique [0002] In recent years, with the widespread application of electronic medical record systems and medical imaging technology in hospitals, massive amounts of medical data have been generated, and disease prediction based on medical data has also become one of the current research hotspots. Human-driven analysis is impractical due to the large volume, high dimensionality, and heterogeneity of medical datasets. Medical imaging disease prediction can be combined with heuristic algorithms to learn classification. A heuristic algorithm is defined as an algorithm based on intuition or empirical construction that gives a feasible solution to the combinatorial optimization problem to be solved within an acceptable cost, and the degree of deviation of the feasible solution from the...

AI Technical Summary

Problems solved by technology

Human-driven analysis is rendered impractical due to the massive, high-dimensionality, and heterogeneity of medical datasets

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