利用集合方法进行糖尿病视网膜病变眼底图像分类

IF 0.7 Q4 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
Marina M. Lukashevich
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引用次数: 0

摘要

摘要 糖尿病视网膜病变会对眼睛视网膜造成损害,导致世界各地的糖尿病患者视力下降。糖尿病视网膜病变会影响患者眼睛的视网膜,开始时无症状,可导致视力完全丧失。通过使用机器学习算法分析视网膜图像,可以相当快速地筛查出这种疾病。早期诊断对于防止失明等危险后果至关重要。本文介绍了集合机器学习算法的实施和比较结果,并描述了一种选择超参数的方法,用于解决筛查问题(二元分类)和糖尿病视网膜病变阶段分类(从 0 到 4)。研究特别关注了在网格上搜索超参数和随机搜索的方法。本研究在结合网格搜索和随机搜索方法的基础上,为集合算法采用了超参数选择机制。超参数的选择以及信息特征的选择使视网膜图像分类的准确率得以提高。实验结果表明,最佳模型(梯度提升,GB)在测试数据集上的视网膜图像分类准确率为 0.7531。当考虑二元分类(是否存在糖尿病视网膜病变)时,准确率达到 0.9400(梯度增强,GB)。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Diabetic Retinopathy Fundus Image Classification Using Ensemble Methods

Diabetic Retinopathy Fundus Image Classification Using Ensemble Methods

Abstract

Diabetic retinopathy causes damage to the retina of the eye and leads to poor vision in patients with diabetes around the world. It affects the retina of a person’s eye, begins asymptomatically, and can lead to complete loss of vision. Screening for this disease can be done fairly quickly by using machine learning algorithms to analyze retinal images. Early diagnosis is crucial to prevent dangerous consequences such as blindness. This paper presents the results of implementation and comparison of ensemble machine learning algorithms and describes an approach to the selection of hyperparameters for solving screening problems (binary classification) and classifying the stage of diabetic retinopathy (from 0 to 4). Particular attention is paid to the approaches of searching for hyperparameters on a lattice and random search. This study uses a hyperparameter selection mechanism for ensemble algorithms based on a combination of grid search and random search approaches. The selection of hyperparameters, as well as the selection of informative features, made it possible to increase the accuracy of classification of retinal images. The experimental results showed an accuracy of 0.7531 for retinal image classification on the test dataset for the best model (gradient boosting, GB). When considering a binary classification (presence or absence of diabetic retinopathy), an accuracy of 0.9400 (gradient boosting, GB) was achieved.

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来源期刊
PATTERN RECOGNITION AND IMAGE ANALYSIS
PATTERN RECOGNITION AND IMAGE ANALYSIS Computer Science-Computer Graphics and Computer-Aided Design
CiteScore
1.80
自引率
20.00%
发文量
80
期刊介绍: The purpose of the journal is to publish high-quality peer-reviewed scientific and technical materials that present the results of fundamental and applied scientific research in the field of image processing, recognition, analysis and understanding, pattern recognition, artificial intelligence, and related fields of theoretical and applied computer science and applied mathematics. The policy of the journal provides for the rapid publication of original scientific articles, analytical reviews, articles of the world''s leading scientists and specialists on the subject of the journal solicited by the editorial board, special thematic issues, proceedings of the world''s leading scientific conferences and seminars, as well as short reports containing new results of fundamental and applied research in the field of mathematical theory and methodology of image analysis, mathematical theory and methodology of image recognition, and mathematical foundations and methodology of artificial intelligence. The journal also publishes articles on the use of the apparatus and methods of the mathematical theory of image analysis and the mathematical theory of image recognition for the development of new information technologies and their supporting software and algorithmic complexes and systems for solving complex and particularly important applied problems. The main scientific areas are the mathematical theory of image analysis and the mathematical theory of pattern recognition. The journal also embraces the problems of analyzing and evaluating poorly formalized, poorly structured, incomplete, contradictory and noisy information, including artificial intelligence, bioinformatics, medical informatics, data mining, big data analysis, machine vision, data representation and modeling, data and knowledge extraction from images, machine learning, forecasting, machine graphics, databases, knowledge bases, medical and technical diagnostics, neural networks, specialized software, specialized computational architectures for information analysis and evaluation, linguistic, psychological, psychophysical, and physiological aspects of image analysis and pattern recognition, applied problems, and related problems. Articles can be submitted either in English or Russian. The English language is preferable. Pattern Recognition and Image Analysis is a hybrid journal that publishes mostly subscription articles that are free of charge for the authors, but also accepts Open Access articles with article processing charges. The journal is one of the top 10 global periodicals on image analysis and pattern recognition and is the only publication on this topic in the Russian Federation, Central and Eastern Europe.
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