@inproceedings{c5dcdb2b9e064d9aa57d596b317aff40,
title = "Retinal hemorrhage detection by rule-based and machine learning approach",
abstract = "Robust detection of hemorrhages (HMs) in color fundus image is important in an automatic diabetic retinopathy grading system. Detection of the hemorrhages that are close to or connected with retinal blood vessels was found to be challenge. However, most methods didn't put research on it, even some of them mentioned this issue. In this paper, we proposed a novel hemorrhage detection method based on rule-based and machine learning methods. We focused on the improvement of detection of the hemorrhages that are close to or connected with retinal blood vessels, besides detecting the independent hemorrhage regions. A preliminary test for detecting HM presence was conducted on the images from two databases. We achieved sensitivity and specificity of 93.3% and 88% as well as 91.9% and 85.6% on the two datasets.",
keywords = "FUNDUS IMAGES",
author = "Di Xiao and Shuang Yu and Janardhan Vignarajan and Dong An and Mei-Ling Tay-Kearney and Yogi Kanagasingam",
year = "2017",
month = sep,
day = "13",
doi = "10.1109/EMBC.2017.8036911",
language = "English",
series = "Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS",
publisher = "IEEE, Institute of Electrical and Electronics Engineers",
pages = "660--663",
booktitle = "2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)",
address = "United States",
note = "2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), EMBC 2017 ; Conference date: 11-07-2017 Through 15-07-2017",
}