Neural-network-based liver cancer auxiliary diagnosis system, method and device, and medium

Abstract

The invention provides a neural-network-based liver cancer auxiliary diagnosis system, method and device, and a medium. The neural-network-based liver cancer auxiliary diagnosis system, method and device convert explicit rules corresponding to the medical knowledge into implicit rules including connection weights by storing medical knowledge and a learning algorithm based on neural networks, and provide corresponding treatment plans for different diseases according to the implicit rules. The neural-network-based liver cancer auxiliary diagnosis system, method and device can improve the diagnosis of liver cancer simply and efficiently, can ensure the reliability of the diagnosis, and can effectively solve the limitations of doctors in the diagnosis and treatment of liver cancer due to doctors' inadequate and untimely grasp of standard diagnosis and treatment of liver cancer and interdiscipline knowledge, thus greatly improving self-learning ability of the system, and realizing constantself-improvement in the process of running.

Description

technical field [0001] This application relates to the field of neural network technology. In particular, it relates to a neural network-based auxiliary diagnosis system, method, device and medium for liver cancer. Background technique [0002] At present, there are many kinds of diagnosis and treatment methods for primary liver cancer in medicine, and their applicable conditions are also relatively complicated, which makes it difficult for clinicians to evaluate the condition of liver cancer patients, and the treatment methods used by different medical institutions are also different. vary. In the course of clinical work, on the one hand, in the face of complex and changeable liver cancer patients, how to break through the limitations of their own knowledge and clinical experience, and choose the most suitable diagnosis and treatment plan for patients is an urgent problem for clinicians to solve. In the process of diagnosis and treatment of liver cancer, the mastery of st...

AI Technical Summary

Problems solved by technology

[0005] 2) The problem of "narrow steps" of knowledge

At present, the expert system can only be applied in a relatively narrow field of knowledge to solve predetermined special problems. Once a problem beyond the scope of knowledge is encountered, it cannot be solved.

[0006] 3) The complexity and efficiency of expert systems

On the one hand, this representation and processing method requires reasonable organization and management of knowledge. On the other hand, the increase in the size of the knowledge base, the increase in the complexity of solving the same problem, and the appearance of "conflicts" during reasoning lead to a combination explosion. The same problem cannot be solved within a limited time, which seriously affects the efficiency of the expert system

[0007] 4) Does not have the function of associative memory. The expert systems developed at present generally do not have the ability of self-learning and associative memory, and cannot self-improvement during operation, and cannot reason through association and memory. Even when the known information has noise , When distortion occurs, there is a lack of effective processing measures

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