Automatic detection of referral patients due to retinal pathologies through data mining-Reference-Cited by-同舟云学术

Automatic detection of referral patients due to retinal pathologies through data mining

Published:2016-04 Issue: Volume:29 Page:47-64
ISSN:1361-8415
Container-title:Medical Image Analysis
language:en
Short-container-title:Medical Image Analysis

Author:

Quellec Gwenolé^ORCID,Lamard Mathieu,Erginay Ali,Chabouis Agnès,Massin Pascale,Cochener Béatrice,Cazuguel Guy

Publisher

Elsevier BV

Subject

Computer Graphics and Computer-Aided Design,Health Informatics,Computer Vision and Pattern Recognition,Radiology, Nuclear Medicine and imaging,Radiological and Ultrasound Technology

Reference70 articles.

1. Automated analysis of retinal images for detection of referable diabetic retinopathy;Abràmoff;JAMA Ophthalmol.,2013

2. Retinal imaging and image analysis;Abràmoff;IEEE Rev. Biomed. Eng.,2010

3. Abràmoff, M. D., Niemeijer, M., Lee, S., Reinhardt, J. 2012. Optimal Registration of Multiple Deformed Images Using a Physical Model of the Imaging Distortion (Patent).

4. Evaluation of a system for automatic detection of diabetic retinopathy from color fundus photographs in a large population of patients with diabetes;Abràmoff;Diabetes Care,2008

5. Automated early detection of diabetic retinopathy;Abràmoff;Ophthalmology,2010

Cited by 35 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Machine Learning and AI Approaches for Analyzing Diabetic and Hypertensive Retinopathy in Ocular Images: A Literature Review;IEEE Access;2024

2. Machine Learning Empowering Personalized Medicine: A Comprehensive Review of Medical Image Analysis Methods;Electronics;2023-10-25

3. Eye Disease Prediction Among Corporate Employees using Machine Learning Techniques;International Journal of Emerging Science and Engineering;2023-09-30

4. Intelligent identification and classification of diabetic retinopathy using fuzzy inference system;Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization;2023-07-19

5. Meta learning for anomaly detection in fundus photographs;Meta Learning With Medical Imaging and Health Informatics Applications;2023