Kidney lump classification method based on random projection

Abstract

The invention relates to a kidney lump classification method based on random projection. The kidney lump classification method comprises the steps: acquiring N pieces of target object data describingkidney lumps; carrying out target area sketching on each CT flat scanning image according to each mask image to obtain a region of interest of each CT flat scanning image, and carrying out radiomics characteristic data extraction on each region of interest to obtain N pieces of radiomics characteristic data; projecting the N radiomics characteristic data through L random projection matrixes to obtain L sets of projection characteristic data; performing multiple classifier training on the L sets of projection characteristic data to obtain a prediction matrix of each classifier and each trainedclassifier, and determining the weight of each classifier; and performing fusion processing on the to-be-classified data by adopting the trained classifiers according to the corresponding weights so as to determine corresponding categories. According to the method, the robustness of the to-be-classified data in the category identification process can be improved, so that the reliability of the classification result is improved.

Description

technical field [0001] The present application relates to the technical field of machine learning, in particular to a random projection-based classification method for small renal masses. Background technique [0002] In recent years, multi-classifier systems have been widely used in the field of machine learning to obtain more reliable and accurate predictions than a single classifier in both supervised and unsupervised learning tasks. It has been successfully applied in many fields such as bioinformatics, remote sensing science, network security, astrophysics, clinical fields, and chemical informatics. Most of the current research on multi-classifier systems can be summarized into the following two categories: non-generative and generative. The non-generative multi-classifier system focuses on the selection of classifiers or the fusion of multi-classifier outputs to optimize the system structure to achieve the purpose of improving the system's predictive ability, while th...

AI Technical Summary

Problems solved by technology

However, by traversing all available models, it is impossible to approach the true optimal solution by trial and error, which makes the traditional classifica

Images