EyeDeep-Net: A Multi-Class Diagnosis of Retinal Diseases using Deep Neural Network
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F23%3APU147016" target="_blank" >RIV/00216305:26220/23:PU147016 - isvavai.cz</a>
Result on the web
<a href="https://link.springer.com/article/10.1007/s00521-023-08249-x" target="_blank" >https://link.springer.com/article/10.1007/s00521-023-08249-x</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/s00521-023-08249-x" target="_blank" >10.1007/s00521-023-08249-x</a>
Alternative languages
Result language
angličtina
Original language name
EyeDeep-Net: A Multi-Class Diagnosis of Retinal Diseases using Deep Neural Network
Original language description
Retinal images are a key element for ophthalmologists in diagnosing a variety of eye illnesses. The retina is vulnerable to microvascular changes as a result of many retinal diseases and a variety of research have been done on early diagnosis of medical images to take proper treatment on time. This paper designs an automated deep learning-based non-invasive framework to diagnose multiple eye diseases using colour fundus images. A multi-class eye disease RFMiD dataset was used to develop an efficient diagnostic framework. Multi-class fundus images were extracted from a multi-label dataset and then various augmentation techniques were applied to make the framework robust in real-time. Images were processed according to the network for low computational demand. A multi-layer neural network EyeDeep-Net has been developed to train and test images for diagnosis of various eye problems in which the keystone convolutional neural network extracts relevant features from the input colour fundus image dataset and then processed features were used to make predictive diagnostic decisions. The strength of the EyeDeep-Net is evaluated using multiple statistical parameters and the performance of the proposed model is found to be significantly superior to multiple baseline state-of-the-art models. A comprehensive comparison of the proposed methodology to the most recent methods proves its efficacy in terms of classification and disease identification through digital fundus images.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
20900 - Industrial biotechnology
Result continuities
Project
<a href="/en/project/VJ02010019" target="_blank" >VJ02010019: Tools for Handwriting fORensics</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2023
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Neural Computing and Applications
ISSN
0941-0643
e-ISSN
1433-3058
Volume of the periodical
35
Issue of the periodical within the volume
3
Country of publishing house
GB - UNITED KINGDOM
Number of pages
21
Pages from-to
10551-10571
UT code for WoS article
000919030000002
EID of the result in the Scopus database
2-s2.0-85146648682