Research Article
Deep Learning Glaucoma Detection Models in Retinal Images Capture by Mobile Devices
@INPROCEEDINGS{10.1007/978-3-031-32029-3_1, author={Roberto Flavio Rezende and Ana Coelho and Rodrigo Fernandes and Jos\^{e} Camara and Alexandre Neto and Ant\^{o}nio Cunha}, title={Deep Learning Glaucoma Detection Models in Retinal Images Capture by Mobile Devices}, proceedings={Wireless Mobile Communication and Healthcare. 11th EAI International Conference, MobiHealth 2022, Virtual Event, November 30 -- December 2, 2022, Proceedings}, proceedings_a={MOBIHEALTH}, year={2023}, month={5}, keywords={Glaucoma Screening Deep Learning Transfer Learning}, doi={10.1007/978-3-031-32029-3_1} }- Roberto Flavio Rezende
Ana Coelho
Rodrigo Fernandes
José Camara
Alexandre Neto
António Cunha
Year: 2023
Deep Learning Glaucoma Detection Models in Retinal Images Capture by Mobile Devices
MOBIHEALTH
Springer
DOI: 10.1007/978-3-031-32029-3_1
Abstract
Glaucoma is a disease that arises from increased intraocular pressure and leads to irreversible partial or total loss of vision. Due to the lack of symptoms, this disease often progresses to more advanced stages, not being detected in the early phase. The screening of glaucoma can be made through visualization of the retina, through retinal images captured by medical equipment or mobile devices with an attached lens to the camera. Deep learning can enhance and increase mass glaucoma screening. In this study, domain transfer learning technique is important to better weight initialization and for understanding features more related to the problem. For this, classic convolutional neural networks, such as ResNet50 will be compared with Vision Transformers, in high and low-resolution images. The high-resolution retinal image will be used to pre-trained the network and use that knowledge for detecting glaucoma in retinal images captured by mobile devices. The ResNet50 model reached the highest values of AUC in the high-resolution dataset, being the more consistent model in all the experiments. However, the Vision Transformer proved to be a promising technique, especially in low-resolution retinal images.
