Premature infant retinopathy plus lesion classification system based on uncertainty

Abstract

The invention provides a premature infant retinopathy plus lesion classification system based on uncertainty, and relates to the technical field of deep learning. The method comprises the following steps: via a vascular segmentation module, firstly, segmenting a blood vessel of a fundus image; using a Bayesian deep learning classification network model to extract features via a classification module; performing dropout Monte Carlo for multiple times; obtaining three groups of probability values corresponding to three lesion types and a group of image noise, calculating the mean value and variance of each group of probability values, taking the lesion type with the maximum mean value as a final classification result, and taking the mean value of the image noise as accidental uncertainty andvariance sum as model uncertainty. When the method is actually put into use, the credibility of the image classification result can be judged through two kinds of uncertainty instead of selecting a diagnosis result given by a blind belief model; for doctors and patients, whether artificial ophthalmology experts are needed for re-diagnosis or not is considered to be very helpful, and the method issafer and more reliable in actual clinical use.

Description

technical field [0001] The invention relates to the technical field of deep learning, in particular to an uncertainty-based classification system for retinopathy of prematurity plus lesions. Background technique [0002] In recent years, with the increasingly mature development of artificial intelligence, algorithms represented by deep learning have shown great advantages in many medical image applications, and have also been widely used in the field of ophthalmology. Among them, it has also been widely used in the classification and grading of retinopathy of prematurity (ROP). [0003] The existing classification and grading method for ROP, for example, the application number is CN201811482400.5, and the patent application document named "Retinopathy of Prematurity Plus Lesion Classification Method" discloses that by constructing a blood vessel segmentation model that can segment blood vessel maps from fundus images , build a classification model that can classify the plus...

AI Technical Summary

Problems solved by technology

[0006] Aiming at the deficiencies of the prior art, the present invention provides a classification system for retinopathy of prematurity plus lesions based on uncertainty, which solves the problem that the prior art cannot show the credibility of the classification results of the model

Images