|
Artificial Intelligence (AI) has gained a prominent role in the medical industry. The rapid development of the computer science
field has caused AI to become a meaningful part of modern healthcare. Image-based analysis involving neural networks is a very important part of eye diagnoses. In this study, a new approach using Convolutional Gated Recurrent Units (GRU) U-Net was proposed
for the classifying healthy cases and cases with retinitis pigmentosa (RP) and cone–rod dystrophy (CORD). The basis for the classification
was the location of pigmentary changes within the retina and fundus autofluorescence (FAF) pattern, as the posterior pole or the periphery
of the retina may be affected. The dataset, gathered in the Chair and Department of General and Pediatric Ophthalmology of Medical
University in Lublin, consisted of 230 ultra-widefield pseudocolour (UWFP) and ultra-widefield FAF images, obtained using the Optos
200TX device (Optos PLC). The data were divided into three categories: healthy subjects (50 images), patients with CORD (48 images) and patients with RP (132 images). For applying deep learning classification, which rely on a large amount of data, the dataset was artificially enlarged using augmentation involving image manipulations. The final dataset contained 744 images. The proposed
Convolutional GRU U-Net network was evaluated taking account of the following measures: accuracy, precision, sensitivity, specificity
and F1. The proposed tool achieved high accuracy in a range of 91.00%–97.90%. The developed solution has a great potential
in RP diagnoses as a supporting tool.
|
