RT Journal Article
T1 Automatic Diagnosis of Ocular Toxoplasmosis from Fundus Images with Residual Neural Networks.
A1 Parra, Rodrigo
A1 Ojeda, Verena
A1 Vázquez Noguera, José Luis
A1 García Torres, Miguel
A1 Mello Román, Julio César
A1 Facon, Jacques
A1 Divina, Federico
A1 Cardozo, Olivia
A1 Castillo, Veronica Elisa
A1 Castro Matto, Ingrid
A2 Universidad Nacional de Asunción - Facultad Politécnica
A2 Universidad Americana (PY)
A2 Universidad Pablo de Olavide (ES)
AB Ocular toxoplasmosis (OT) is commonly diagnosed through the analysis of fundus images of the eye by a specialist. Despite Deep Learning being widely used to process and recognize pathologies in medical images, the diagnosis of ocular toxoplasmosis(OT) has not yet received much attention. A predictive computational model is a valuable time-saving option if used as a support tool for the diagnosis of OT. It could also help diagnose atypical cases, being particularly useful for ophthalmologists who have less experience. In this work, we propose the use of a deep learning model to perform automatic diagnosis of ocular toxoplasmosis from images of the eye fundus. A pretrained residual neural network is fine-tuned on a dataset of samples collected at the medical center of Hospital de Clínicas in Asunción, Paraguay. With sensitivity and specificity rates equal to 94% and 93%,respectively, the results show that the proposed model is highly promising. In order to replicate the results and advance further in this area of research, an open data set of images of the eye fundus labeled by ophthalmologists is made available.
YR 2021
FD 2021
LK http://hdl.handle.net/20.500.14066/3794
UL http://hdl.handle.net/20.500.14066/3794
LA eng
NO CONACYT - Consejo Nacional de Ciencia y Tecnología
DS MINDS@UW
RD 24-dic-2024