Abstract
Subject of research: is the study of methods for analyzing perimetric images for the diagnosis and control of glaucoma diseases. Objects of research: is a dataset collected on the ophthalmological perimeter with the results of various patient pathologies, since the ophthalmological community is acutely aware of the issue of disease control and import substitution. [5]. Purpose of research: is to consider various machine learning methods that can classify glaucoma. This is possible thanks to the classifier built after labeling the dataset. It is able to determine from the image whether the visual fields depicted on it are the results of the impact of glaucoma on the eyes or other visual diseases. Earlier in the work [3], a dataset was described that was collected on the Tomey perimeter. The average age of the examined patients ranged from 30 to 85 years. Methods of research: machine learning methods for classifying image results (stochastic gradient descent, logistic regression, random forest, naive Bayes). Main results of research: the result of the study is computer modeling that can determine from the image whether the result is glaucoma or another disease (binary classification).
Abstract (translated)
研究主题：该研究旨在探讨分析周边视野图像以诊断和监控青光眼疾病的方法。 研究对象：是一组在眼科视野计上收集的数据集，包含各种患者病状的结果。眼科界对于疾病的控制和进口替代问题十分关注。 研究目的：评估能够分类青光眼的各种机器学习方法。通过给数据集贴标签后建立的分类器，可以从图像中判断出描绘的视觉场是否受到青光眼的影响或其它视网膜疾病影响。之前的工作[3]描述了一个在Tomey视野计上收集的数据集。接受检查患者的平均年龄范围是30至85岁。 研究方法：使用机器学习方法对图像结果进行分类（随机梯度下降、逻辑回归、随机森林、朴素贝叶斯）。 主要研究成果：该研究的结果是一种计算机模拟模型，能够从图像中判断出视觉场是否为青光眼所致或其他疾病（二元分类）。
URL
https://arxiv.org/abs/2503.14562
