Unveiling visionary frontiers: a survey of cutting-edge techniques in deep learning for retinal disease diagnosis

Rajatha Rajatha, Ashoka Davanageri Virupakshappa


Retinal disorders impact millions of people globally. These disorders can be detected and diagnosed early enough to not only cure but also avoid permanent blindness. Manual identification of these diseases has always been tedious, time-consuming, and inconsistent. For ophthalmologists, retinal fundus images are a valuable source of information in diagnosing retinal diseases. Automatic identification of eye disorders using artificial intelligence (AI) based learning models has seen substantial development in the computer vision sector recently. Various models, particularly deep learning (DL) models are incredible in identifying and classifying diseases. In the presented review, we have performed an in-depth analysis of various existing DL models, involving preprocessing, classification, segmentation, and techniques to deal with data imbalance. We have also endeavored to gauge the effectiveness of these models by evaluating their performance using the metrics employed in their assessment. In addition, we explored various challenges along with the potential future scope in this domain.



Age-related macular disease; Convolutional neural network; Diabetic retinopathy; Ensemble learning; Glaucoma screening; Multi-label classification; Transfer learning

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DOI: http://doi.org/10.11591/ijeecs.v33.i2.pp1261-1272


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The Indonesian Journal of Electrical Engineering and Computer Science (IJEECS)
p-ISSN: 2502-4752, e-ISSN: 2502-4760
This journal is published by the Institute of Advanced Engineering and Science (IAES) in collaboration with Intelektual Pustaka Media Utama (IPMU).

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