Please use this identifier to cite or link to this item: http://hdl.handle.net/10316/100877
Title: Epiretinal Membrane Detection in Optical Coherence Tomography Retinal Images Using Deep Learning
Authors: Parra-Mora, Esther 
Cazañas-Gordón, Alex 
Proença, Rui 
da Silva Cruz, Luis A. 
Keywords: Artificial intelligence; deep learning; epiretinal membrane; macular puker; neural networks; optical coherence tomography; transfer learning
Issue Date: 2021
Project: Secretariat of Higher Education, Science, Technology and Innovation of the Republic of Ecuador 
FCT UIDB/EEA/50008/2020 
Serial title, monograph or event: IEEE Access
Volume: 9
Abstract: Epiretinal membrane (ERM) is an eye disease that affects 7% of the world population, with a higher incidence in people over 75 years old. If left untreated, it can lead to complications in the central vision, resulting in severe vision loss. Early detection is important for progress follow-up, treatment monitoring, and to avoid total vision loss. Optical coherence tomography, a non-invasive retina imaging technique, can be used for effective detection and monitoring of this condition. To date, automatic methods to detect ERM have received little attention in the research literature. This article describes the application of deep learning to the automatic detection of ERM. The proposed solution is based on four widely used convolutional neural network architectures adapted to the task using transfer learning, and ne-tuned with a proprietary dataset. The architectures were specialized by optimizing the network hyperparameters and two loss functions, cross-entropy and focal loss.Adetailed description of the methods is provided, complemented with an exhaustive evaluation of their performance. Overall, the methods reached an accuracy of 99.7%, with sensitivity and speci city of 99.47% and 99.93%, respectively. The results showed that transfer learning enabled a successful use of deep learning to detect ERM in optical coherence tomography retinal images, even when only relatively small training datasets are available.
URI: http://hdl.handle.net/10316/100877
ISSN: 2169-3536
DOI: 10.1109/ACCESS.2021.3095655
Rights: openAccess
Appears in Collections:FMUC Medicina - Artigos em Revistas Internacionais
I&D IT - Artigos em Revistas Internacionais
FCTUC Eng.Electrotécnica - Artigos em Revistas Internacionais

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