A novel multimodality based dual fusion integrated approach for efficient and early prediction of glaucoma

人工智能 计算机科学 随机森林 水准点(测量) 机器学习 支持向量机 特征(语言学) 多模态 深度学习 分类 青光眼 集成学习 模式识别(心理学) 医学 语言学 大地测量学 万维网 眼科 地理 哲学
作者
Law Kumar Singh,Munish Khanna,. Pooja
出处
期刊:Biomedical Signal Processing and Control [Elsevier BV]
卷期号:73: 103468-103468 被引量:15
标识
DOI:10.1016/j.bspc.2021.103468
摘要

As there is currently no exact treatment for glaucoma, early detection and diagnosis are essential to reduce the risk of this infection. In recent years, Machine learning and deep learning has significantly improved prediction and classification of human diseases. We are the first to offer a new multimodal approach for glaucoma prediction in this article. We shortlisted three public datasets and in totality we tested seven combinations of these datasets. Initially, we created five multimodal representations of each publicly accessible benchmark dataset. In the first vertical, we extracted 36 critical features from each multimodal of a particular dataset. These extracted features are subsequently fused (referred to as early fusion) to create each dataset's 180 features. These 180 features are ranked using random forest. The top 50% of the features are retrieved to create a feature vector. This feature vector is fed into different machine learning classifiers and their ensemble model for classification purposes. In the second vertical, we worked at the picture level where we send images from each dataset's five multimodal dimensions to two deep learning methods for classification purposes. For each of the seven experiments conducted in this study we obtain several sets of findings. These categorization findings are combined (referred to as late fusion) and submitted to professional ophthalmologists who make the final determination based on their judgments. As a consequence of the proposed approach, we now have a computerized glaucoma diagnostic system with remarkable results (accuracy upto 95.56%).
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
更新
PDF的下载单位、IP信息已删除 (2025-6-4)

科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
研友_X89o6n完成签到,获得积分10
2秒前
Ther发布了新的文献求助10
4秒前
哈哈哈完成签到,获得积分10
5秒前
7秒前
诚心的初露完成签到,获得积分10
7秒前
lyb完成签到 ,获得积分10
9秒前
风中方盒完成签到,获得积分20
9秒前
布丁圆团完成签到,获得积分10
10秒前
yikeshu完成签到,获得积分10
10秒前
Zoe完成签到 ,获得积分10
11秒前
13秒前
星辰大海应助do0采纳,获得10
14秒前
tt完成签到 ,获得积分10
15秒前
浅辰完成签到,获得积分10
16秒前
大气萤完成签到,获得积分20
17秒前
17秒前
我ppp完成签到 ,获得积分10
17秒前
18秒前
易燃物品完成签到,获得积分10
18秒前
Hello应助Ther采纳,获得10
20秒前
CherylZhao完成签到,获得积分10
21秒前
Galato发布了新的文献求助10
22秒前
颜愫完成签到,获得积分10
22秒前
安详向日葵完成签到 ,获得积分10
23秒前
拼搏的白云完成签到,获得积分10
23秒前
852应助hhh采纳,获得10
23秒前
李白白白完成签到,获得积分10
23秒前
王手完成签到,获得积分10
23秒前
24秒前
一人完成签到,获得积分10
25秒前
do0完成签到,获得积分10
26秒前
yar应助xlz110采纳,获得10
26秒前
NexusExplorer应助落寞凌波采纳,获得10
28秒前
量子星尘发布了新的文献求助10
31秒前
123完成签到 ,获得积分10
31秒前
哈哈呵完成签到,获得积分10
31秒前
31秒前
Rylee完成签到,获得积分10
31秒前
Jiro完成签到,获得积分10
33秒前
我ppp发布了新的文献求助60
34秒前
高分求助中
【提示信息,请勿应助】关于scihub 10000
Les Mantodea de Guyane: Insecta, Polyneoptera [The Mantids of French Guiana] 3000
徐淮辽南地区新元古代叠层石及生物地层 3000
The Mother of All Tableaux: Order, Equivalence, and Geometry in the Large-scale Structure of Optimality Theory 3000
Handbook of Industrial Diamonds.Vol2 1100
Global Eyelash Assessment scale (GEA) 1000
Picture Books with Same-sex Parented Families: Unintentional Censorship 550
热门求助领域 (近24小时)
化学 材料科学 医学 生物 工程类 有机化学 生物化学 物理 内科学 纳米技术 计算机科学 化学工程 复合材料 遗传学 基因 物理化学 催化作用 冶金 细胞生物学 免疫学
热门帖子
关注 科研通微信公众号,转发送积分 4038368
求助须知:如何正确求助?哪些是违规求助? 3576068
关于积分的说明 11374313
捐赠科研通 3305780
什么是DOI,文献DOI怎么找? 1819322
邀请新用户注册赠送积分活动 892672
科研通“疑难数据库(出版商)”最低求助积分说明 815029