Xerophthalmia grading system based on regional adaptive attention network

Abstract

The invention discloses a xerophthalmia grading system based on a regional adaptive attention network. The system comprises a computer memory, a computer processor and a computer program which is stored in the computer memory and can be executed on the computer processor, a trained xerophthalmia grading model is stored in the computer memory, and the xerophthalmia grading model is based on a regional adaptive attention network; when the computer processor executes the computer program, the following steps are realized: obtaining a to-be-tested original eyelid plate infrared image to carry outgray scale preprocessing, and carrying out bilateral filtering processing on a single-channel gray scale image obtained after preprocessing; and inputting the processed image into a xerophthalmia grading model to obtain an eyelid plate positioning and xerophthalmia grading result. According to the invention, automatic analysis of the eyelid plate infrared photograph can be realized, and auxiliarydiagnosis of xerophthalmia grading can be effectively carried out.

Description

technical field [0001] The invention belongs to the field of medical image analysis and machine learning, and in particular relates to a dry eye syndrome grading system based on an area self-adaptive attention network. Background technique [0002] Dry eye is a multifactorial disease of the ocular surface, characterized by loss of tear film homeostasis, accompanied by ocular surface symptoms, and its pathogenesis includes tear film instability, tear hyperosmolarity, ocular surface inflammation and loss, and neurosensory abnormalities. Many studies have shown that the incidence of dry eye fluctuates from 5 to 50%. [0003] The traditional way of diagnosing and evaluating dry eye is to use ocular surface disease index (Ocular Surface Disease Index, OSDI), NIBUT, tear river height, meibomian gland morphology and other indicators, which can objectively diagnose and classify dry eye. However, the shortcomings of this evaluation method are: there are many relevant evaluation indi...

AI Technical Summary

Problems solved by technology

[0006] In order to overcome the shortcomings of the existing dry eye classification that requires multiple detection aids, low efficiency, and low precision, the present invention proposes a fast, high-efficiency, and high-precision dry eye syndrome based on a region-adaptive attention network The grading system realizes the automatic analysis of the infrared photos of the eyelid plate, which can effectively assist the diagnosis of dry eye grading

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