卷积神经网络用于眼底图像中糖尿病视网膜病变的自动诊断

S. Rama, Naresh Cherukuri, D. Kumar, R. Jayakarthik, B. Nagarajan, A. Balaram, G. Jyothi, Y. Kumar, Email S. Rama Krishna
{"title":"卷积神经网络用于眼底图像中糖尿病视网膜病变的自动诊断","authors":"S. Rama, Naresh Cherukuri, D. Kumar, R. Jayakarthik, B. Nagarajan, A. Balaram, G. Jyothi, Y. Kumar, Email S. Rama Krishna","doi":"10.37965/jait.2023.0264","DOIUrl":null,"url":null,"abstract":"Diabetic retinopathy (DR), a long-term complication of diabetes, is notoriously hard to detect in its early stages due to the fact that it only shows a subset of symptoms. Standard diagnostic procedures for DR now include OCT and digital fundus imaging. If digital fundus images alone could provide a reliable diagnosis, then eliminating the costly optical coherence tomography would be beneficial for all parties involved. Optometrists and their patients will find this useful. Using deep convolutional neural networks, we provide a novel approach to this problem. Our approach deviates from standard DCNN methods by exchanging typical max-pooling layers with fractional max-pooling ones. In order to collect more subtle information for categorisation, two such DCNNs, each with a different number of layers, are trained. To establish these limits, we use deep convolutional neural networks (DCNNs) and features extracted from picture metadata to train a support vector machine classifier. In our experiments, we used information from Kaggle's open DR detection database. We fed our model 34,124 training images, 1,000 validation examples, and 53,572 test images to train and test it. Each of the five classes in the proposed DR classifier corresponds to one of the steps in the DR process and is given a numeric value between 0 and 4. Experimental results show a higher identification rate (86.17%) than those found in the existing literature, indicating the suggested strategy may be effective. We have jointly developed an algorithm for machine learning and accompanying software, and we've named it Deep Retina. Images of the fundus acquired by the typical person using a portable ophthalmoscope may be instantly analyzed using our technology. This technology might be used for self-diagnosis, at-home care, and telemedicine.","PeriodicalId":70996,"journal":{"name":"人工智能技术学报(英文)","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2023-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Convolutional Neural Networks for Automated Diagnosis of Diabetic Retinopathy in Fundus Images\",\"authors\":\"S. Rama, Naresh Cherukuri, D. Kumar, R. Jayakarthik, B. Nagarajan, A. Balaram, G. Jyothi, Y. Kumar, Email S. Rama Krishna\",\"doi\":\"10.37965/jait.2023.0264\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Diabetic retinopathy (DR), a long-term complication of diabetes, is notoriously hard to detect in its early stages due to the fact that it only shows a subset of symptoms. Standard diagnostic procedures for DR now include OCT and digital fundus imaging. If digital fundus images alone could provide a reliable diagnosis, then eliminating the costly optical coherence tomography would be beneficial for all parties involved. Optometrists and their patients will find this useful. Using deep convolutional neural networks, we provide a novel approach to this problem. Our approach deviates from standard DCNN methods by exchanging typical max-pooling layers with fractional max-pooling ones. In order to collect more subtle information for categorisation, two such DCNNs, each with a different number of layers, are trained. To establish these limits, we use deep convolutional neural networks (DCNNs) and features extracted from picture metadata to train a support vector machine classifier. In our experiments, we used information from Kaggle's open DR detection database. We fed our model 34,124 training images, 1,000 validation examples, and 53,572 test images to train and test it. Each of the five classes in the proposed DR classifier corresponds to one of the steps in the DR process and is given a numeric value between 0 and 4. Experimental results show a higher identification rate (86.17%) than those found in the existing literature, indicating the suggested strategy may be effective. We have jointly developed an algorithm for machine learning and accompanying software, and we've named it Deep Retina. Images of the fundus acquired by the typical person using a portable ophthalmoscope may be instantly analyzed using our technology. This technology might be used for self-diagnosis, at-home care, and telemedicine.\",\"PeriodicalId\":70996,\"journal\":{\"name\":\"人工智能技术学报(英文)\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-08-24\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"人工智能技术学报(英文)\",\"FirstCategoryId\":\"1093\",\"ListUrlMain\":\"https://doi.org/10.37965/jait.2023.0264\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"人工智能技术学报(英文)","FirstCategoryId":"1093","ListUrlMain":"https://doi.org/10.37965/jait.2023.0264","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

摘要

糖尿病视网膜病变(DR)是糖尿病的一种长期并发症,由于它只表现出一部分症状,在早期很难发现。DR的标准诊断程序现在包括OCT和数字眼底成像。如果仅凭数字眼底图像就可以提供可靠的诊断,那么消除昂贵的光学相干断层扫描对所有相关方都是有益的。验光师和他们的病人会发现这很有用。使用深度卷积神经网络,我们为这个问题提供了一种新的方法。我们的方法偏离了标准的DCNN方法,将典型的最大池层与分数最大池层交换。为了收集更微妙的信息进行分类,训练了两个这样的DCNN,每个DCNN具有不同的层数。为了建立这些限制,我们使用深度卷积神经网络(DCNN)和从图片元数据中提取的特征来训练支持向量机分类器。在我们的实验中,我们使用了来自Kaggle开放DR检测数据库的信息。我们给我们的模型提供了34124个训练图像、1000个验证示例和53572个测试图像来训练和测试它。所提出的DR分类器中的五个类中的每一个都对应于DR过程中的一个步骤,并给出了0到4之间的数值。实验结果表明,与现有文献相比,识别率更高(86.17%),表明所提出的策略可能是有效的。我们共同开发了一种用于机器学习的算法和相关软件,并将其命名为Deep Retina。使用我们的技术可以立即分析由典型的人使用便携式检眼镜获得的眼底图像。这项技术可能用于自我诊断、家庭护理和远程医疗。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Convolutional Neural Networks for Automated Diagnosis of Diabetic Retinopathy in Fundus Images
Diabetic retinopathy (DR), a long-term complication of diabetes, is notoriously hard to detect in its early stages due to the fact that it only shows a subset of symptoms. Standard diagnostic procedures for DR now include OCT and digital fundus imaging. If digital fundus images alone could provide a reliable diagnosis, then eliminating the costly optical coherence tomography would be beneficial for all parties involved. Optometrists and their patients will find this useful. Using deep convolutional neural networks, we provide a novel approach to this problem. Our approach deviates from standard DCNN methods by exchanging typical max-pooling layers with fractional max-pooling ones. In order to collect more subtle information for categorisation, two such DCNNs, each with a different number of layers, are trained. To establish these limits, we use deep convolutional neural networks (DCNNs) and features extracted from picture metadata to train a support vector machine classifier. In our experiments, we used information from Kaggle's open DR detection database. We fed our model 34,124 training images, 1,000 validation examples, and 53,572 test images to train and test it. Each of the five classes in the proposed DR classifier corresponds to one of the steps in the DR process and is given a numeric value between 0 and 4. Experimental results show a higher identification rate (86.17%) than those found in the existing literature, indicating the suggested strategy may be effective. We have jointly developed an algorithm for machine learning and accompanying software, and we've named it Deep Retina. Images of the fundus acquired by the typical person using a portable ophthalmoscope may be instantly analyzed using our technology. This technology might be used for self-diagnosis, at-home care, and telemedicine.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
CiteScore
8.70
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信