Eye Deep-Net: a deep neural network-based multi-class retinal disease diagnostic

Ophthalmologists rely heavily on retinal pictures to diagnose a wide range of eye conditions. Numerous retinal disorders may lead to microvascular alterations in the retina, and a number of studies have been conducted on the early identification of medical pictures to enable prompt and appropriate t...

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Bibliographic Details
Published in:Turkish journal of computer and mathematics education 2024-09, Vol.15 (3), p.80-90
Main Authors: Jawalkar, Anil P, Navya, D, Himasriya, E, Sree, Y Harika
Format: Article
Language:English
Subjects:
Artificial neural networks
Color
Data augmentation
Datasets
Diagnostic systems
Digital imaging
Eye (anatomy)
Illnesses
Machine learning
Medical imaging
Multilayers
Networks
Neural networks
Pictures
Real time
State-of-the-art reviews
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Summary:Ophthalmologists rely heavily on retinal pictures to diagnose a wide range of eye conditions. Numerous retinal disorders may lead to microvascular alterations in the retina, and a number of studies have been conducted on the early identification of medical pictures to enable prompt and appropriate treatment. In order to identify various eye illnesses using color fundus pictures, this study develops a non-invasive, automated deep learning system. A multiclass ocular illness A productive diagnostic approach was created using the Remind dataset. A variety of augmentation strategies were used to make the structure robust in real-time after multi-class fundus pictures were collected from a multi-label dataset. Low computational demand images were processed in accordance with the network. The fundamental convolutional neural network (CNN) extracts appropriate characteristics from the input color fundus image dataset, and then processed characteristics were employed to make predictive diagnoses. This multi-layer neural network, called Eye Deep-Net, has been developed for training and evaluating images for the recognition of various eye problems. The performance of the suggested model is determined to be much better than numerous baseline state-of-the-art models. The strength from the Eye Deep-Net is assessed using different statistical metrics. The suggested methodology's effectiveness in classifying and identifying diseases using digital fundus pictures is shown by a thorough comparison with the most modern techniques.
ISSN:1309-4653
DOI:10.61841/turcomat.v15i3.14780