Intelligent classification system and method for pathological type of lymphoma

Abstract

The invention relates to an intelligent classification system and method for the pathological type of lymphoma. A doctor workstation is used to receive input information and display a classification result. A server comprises an information acquisition module, a preprocessing module, a model training module, an information receiving module and a lymphoma pathological classifier. The information acquisition module acquires electronic data to acquire a training set from the data. The preprocessing module is used to extract characteristic pathological information, preprocess the information and generate a corresponding characteristic value for a characteristic word. The model training module uses the characteristic value to train a classification analysis model to acquire a trained lymphoma pathological classifier. The information receiving module receives information input by a user through the doctor workstation and transmits the information to the preprocessing module. The trained lymphoma pathological classifier acquires a lymphoma pathological type classification result. The system and method realize automatic classification, save manpower, and do not rely on the subjective judgment of a doctor.

Description

technical field [0001] The invention relates to the technical field of machine learning, in particular to an intelligent classification system and method for lymphoma pathological types. Background technique [0002] At present, we are in the era of big data, and there are large-scale data everywhere, and the simple rule processing in the existing technology is difficult to play the value of these data. The rapid development of hardware provides conditions for the application of big data. High-performance computing greatly reduces the data learning time and data processing cost based on large-scale data; large-scale data storage makes it possible to process large-scale data faster and at a lower cost. Due to the development of hardware and algorithms, after using machine learning to solve data analysis problems, more lucrative benefits can be obtained. [0003] Existing machine learning technologies are mainly applied to Internet fields such as news and e-commerce, but are...

AI Technical Summary

Problems solved by technology

Therefore, the present invention provides a method for intelligently classifying lymphoma pathological types by applying machine learning methods to the medical field, so as to make up for the prior art. The classification of lymphoma mainly depends on the subjective judgment of doctors, and the accuracy of judgment depends on Due to the doctor's work experience, different doctors may have inconsistent judgments on the patient's condition, which may easily lead to misdiagnosis and may even delay the diagnosis of the patient's condition.

Images