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The increasing development of Deep Learning mechanism allowed ones to create semi-fully or fully automated diagnosis
software solutions for medical imaging diagnosis. The convolutional neural networks are widely applied for central retinal diseases classifi-
cation based on OCT images. The main aim of this study is to propose a new network, Deep CNN-GRU for classification of early-stage
and end-stages macular diseases as age-related macular degeneration and diabetic macular edema (DME). Three types of disorders have
been taken into consideration: drusen, choroidal neovascularization (CNV), DME, alongside with normal cases. The created automatic tool
was verified on the well-known Labelled Optical Coherence Tomography (OCT) dataset. For the classifier evaluation the following
measures were calculated: accuracy, precision, recall, and F1 score. Based on these values, it can be stated that the use of a GRU layer
directly connected to a convolutional network plays a pivotal role in improving previously achieved results. Additionally, the proposed tool
was compared with the state-of-the-art of deep learning studies performed on the Labelled OCT dataset. The Deep CNN-GRU network
achieved high performance, reaching up to 98.90% accuracy. The obtained results of classification performance place the tool
as one of the top solutions for diagnosing retinal diseases, both early and late stage.
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