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AuthorDevecioglu O.C.
AuthorMalik J.
AuthorInce T.
AuthorKiranyaz, Mustafa Serkan
AuthorAtalay E.
AuthorGabbouj M.
Available date2022-04-26T12:31:18Z
Publication Date2021
Publication NameIEEE Access
ResourceScopus
Identifierhttp://dx.doi.org/10.1109/ACCESS.2021.3118102
URIhttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85118196905&doi=10.1109%2fACCESS.2021.3118102&partnerID=40&md5=5b585cf6eb951060d376a8e8259fece1
URIhttp://hdl.handle.net/10576/30594
AbstractGlaucoma leads to permanent vision disability by damaging the optical nerve that transmits visual images to the brain. The fact that glaucoma does not show any symptoms as it progresses and cannot be stopped at the later stages, makes it critical to be diagnosed in its early stages. Although various deep learning models have been applied for detecting glaucoma from digital fundus images, due to the scarcity of labeled data, their generalization performance was limited along with high computational complexity and special hardware requirements. In this study, compact Self-Organized Operational Neural Networks (Self-ONNs) are proposed for early detection of glaucoma in fundus images and their performance is compared against the conventional (deep) Convolutional Neural Networks (CNNs) over three benchmark datasets: ACRIMA, RIM-ONE, and ESOGU. The experimental results demonstrate that Self-ONNs not only achieve superior detection performance but can also significantly reduce the computational complexity making it a potentially suitable network model for biomedical datasets especially when the data is scarce.
Languageen
PublisherInstitute of Electrical and Electronics Engineers Inc.
SubjectBenchmarking
Complex networks
Computational complexity
Convolution
Convolutional neural networks
Deep learning
E-learning
Medical imaging
Optical data processing
Convolutional neural network
Convolutional neural network: glaucoma detection
Digital fundus images
Glaucoma detection
Medical images processing
Neural-networks
Operational neural network
Real- time
Self-organised
Transfer learning
Ophthalmology
TitleReal-Time Glaucoma Detection from Digital Fundus Images Using Self-ONNs
TypeArticle
Pagination140031-140041
Volume Number9
dc.accessType Abstract Only


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