用机器学习分析眼底视野图像，区分青光眼与其他眼部疾病
Analysis of human visual field information using machine learning methods and assessment of their accuracy
- 采用多种机器学习模型分析视野检查图像
- 实现青光眼与其它视觉疾病二分类准确率超90%
- 适合眼科医生和医学人工智能研究者参考
研究对象为基于眼底视野计的图像分析方法，用于青光眼的诊断与监测。数据集来自Tomey视野计，涵盖30至85岁患者的不同病理结果。研究目的为评估多种机器学习方法在青光眼分类中的表现。通过标注数据构建分类器，可判断视野图像是否由青光眼引起。研究方法包括随机梯度下降、逻辑回归、随机森林与朴素贝叶斯等算法。主要成果是实现了对视野图像中青光眼与非青光眼病变的计算机辅助二分类建模。
原文摘要 · Abstract (English)
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).
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