Image grading method, device and equipment and storage medium

Abstract

The invention discloses an image grading method, device and equipment and a storage medium. The method comprises the steps: determining an original three-dimensional image corresponding to an originalAS-OCT image; sequentially inputting the intermediate three-dimensional images of the first preset number scale corresponding to the original three-dimensional image into a corresponding preset 3D convolutional neural network to obtain a corresponding one-dimensional vector; performing calculating according to the first preset number of one-dimensional vectors to obtain a corresponding output result; and determining the turbidity degree of the original AS-OCT image according to the output result and a pre-configured turbidity category. According to the invention, the original AS-OCT image isshot from different angles, more features in the image can be extracted and learned, and the network classification precision is effectively improved; meanwhile, by constructing a multi-scale 3D convolutional neural network, intermediate three-dimensional images of multiple scales corresponding to an original three-dimensional image are input into a corresponding preset 3D convolutional neural network, so global features and local features are fused to facilitate network mining to obtain more discriminative feature information.

Description

technical field [0001] Embodiments of the present invention relate to heart rhythm recognition technology, and in particular to an image classification method, device, equipment and storage medium. Background technique [0002] Cataract is an eye disease in which the lens protein is denatured and clouded due to the metabolic disorder of the lens, and the patient's vision is cloudy and blurred. It is therefore important to grade the opacity of some ocular structures of the eye. [0003] Currently, the degree of cataract opacity can be graded using fundus images and slit-lamp images. figure 1 It is a schematic display diagram of a fundus image provided by the prior art; figure 2 It is a schematic display diagram of a slit lamp diagram provided by the prior art. Since both the fundus image and the slit lamp image are two-dimensional images, if the neural network is used to classify the degree of turbidity of the fundus image or slit lamp image, the accuracy of automatic ima...

AI Technical Summary

Problems solved by technology

Since both the fundus image and the slit lamp image are two-dimensional images, if the neural network is used to grade the degree of turbidity of the fundus image or slit lamp image, the accuracy of automatic image grading will be reduced

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