Revolutionizing diabetic eye disease detection: retinal image analysis with cutting-edge deep learning techniques
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62690094%3A18470%2F24%3A50022193" target="_blank" >RIV/62690094:18470/24:50022193 - isvavai.cz</a>
Result on the web
<a href="https://peerj.com/articles/cs-2186/" target="_blank" >https://peerj.com/articles/cs-2186/</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.7717/peerj-cs.2186" target="_blank" >10.7717/peerj-cs.2186</a>
Alternative languages
Result language
angličtina
Original language name
Revolutionizing diabetic eye disease detection: retinal image analysis with cutting-edge deep learning techniques
Original language description
Globally, glaucoma is a leading cause of visual impairment and vision loss, emphasizing the critical need for early diagnosis and intervention. This research explores the application of deep learning for automated glaucoma diagnosis using retinal fundus photographs. We introduce a novel cross-sectional optic nerve head (ONH) feature derived from optical coherence tomography (OCT) images to enhance existing diagnostic procedures. Our approach leverages deep learning to automatically detect key optic disc characteristics, eliminating the need for manual feature engineering. The deep learning classifier then categorizes images as normal or abnormal, streamlining the diagnostic process. Deep learning techniques have proven effective in classifying and segmenting retinal fundus images, enabling the analysis of a growing number of images. This study introduces a novel mixed loss function that combines the strengths of focal loss and correntropy loss to handle complex biomedical data with class imbalance and outliers, particularly in OCT images. We further refine a multi-task deep learning model that capitalizes on similarities across major eye-fundus activities and metrics for glaucoma detection. The model is rigorously evaluated on a real-world ophthalmic dataset, achieving impressive accuracy, specificity, and sensitivity of 100%, 99.8%, and 99.2%, respectively, surpassing state-of-the-art methods. These promising results underscore the potential of our deep learning algorithm for automated glaucoma diagnosis, with significant implications for clinical applications. By simultaneously addressing segmentation and classification challenges, our approach demonstrates its effectiveness in accurately identifying ocular diseases, paving the way for improved glaucoma diagnosis and early intervention.
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
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
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Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2024
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
PeerJ Computer Science
ISSN
2376-5992
e-ISSN
2376-5992
Volume of the periodical
10
Issue of the periodical within the volume
September
Country of publishing house
GB - UNITED KINGDOM
Number of pages
20
Pages from-to
"Article Number: e2186"
UT code for WoS article
001320230400001
EID of the result in the Scopus database
2-s2.0-85204776395