Thesis Details
Detekce patologií na snímcích sítnice oka
The main goal of this work is to design and implement an algorithm for the detection of microaneurysms, hard exudates, and soft exudates on color fundus images. An algorithm for detecting objects based on deep learning has been proposed. The Faster R-CNN architecture with a feature pyramid network and a pre-pretrained residual network was used together with various data transformation methods. A total of six retinal image datasets were used to train, validate and test the models. The trained models achieved 0.46 mean average accuracy (mAP) in microaneurysm detection and 0.48 mAP in exudates detection during testing. The resulting models have been compared with published articles and make it possible to detect given pathologies with commendable accuracy.
retinal pathology detection, color fundus images, microaneurysms, exudates, hard exudates, soft exudates, cotton wool spots, deep learning, Faster R-CNN, pretrained network, feature pyramid network, average precision
Hradiš Michal, Ing., Ph.D. (DCGM FIT BUT), člen
Jaroš Jiří, doc. Ing., Ph.D. (DCSY FIT BUT), člen
Křivka Zbyněk, Ing., Ph.D. (DIFS FIT BUT), člen
Lengál Ondřej, Ing., Ph.D. (DITS FIT BUT), člen
@bachelorsthesis{FITBT24911,
author = "David Hurta",
type = "Bachelor's thesis",
title = "Detekce patologi\'{i} na sn\'{i}mc\'{i}ch s\'{i}tnice oka",
school = "Brno University of Technology, Faculty of Information Technology",
year = 2022,
location = "Brno, CZ",
language = "czech",
url = "https://www.fit.vut.cz/study/thesis/24911/"
}