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AuthorHassan, Syed Ale
AuthorAkbar, Shahzad
AuthorKhan, Habib Ullah
Available date2024-04-30T11:12:14Z
Publication Date2023-08-02
Publication NameMultimedia Tools and Applications
Identifierhttp://dx.doi.org/10.1007/s11042-023-16206-y
CitationHassan, S. A., Akbar, S., & Khan, H. U. (2024). Detection of central serous retinopathy using deep learning through retinal images. Multimedia Tools and Applications, 83(7), 21369-21396.
ISSN1380-7501
URIhttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85166567863&origin=inward
URIhttp://hdl.handle.net/10576/54519
AbstractThe human eye is responsible for the visual reorganization of objects in the environment. The eye is divided into different layers and front/back areas; however, the most important part is the retina, responsible for capturing light and generating electrical impulses for further processing in the brain. Several manual and automated methods have been proposed to detect retinal diseases, though these techniques are time-consuming, inefficient, and unpleasant for patients. This research proposes a deep learning-based CSR detection employing two imaging techniques: OCT and fundus photography. These input images are manually augmented before classification, followed by training of DarkNet and DenseNet networks through both datasets. Moreover, pre-trained DarkNet and DenseNet classifiers are modified according to the need. Finally, the performance of both networks on their datasets is compared using evaluation parameters. After several experiments, the best accuracy of 99.78%, the sensitivity of 99.6%, specificity of 100%, and the F1 score of 99.52% were achieved through OCT images using the DenseNet network. The experimental results demonstrate that the proposed model is effective and efficient for CSR detection using the OCT dataset and suitable for deployment in clinical applications.
SponsorThis work was supported by the Riphah Artificial Intelligence Research (RAIR) Lab, Riphah International University, Faisalabad Campus, Pakistan. Open Access funding provided by the Qatar National Library. Qatar National Library and Qatar University Internal Grant IRCC-2021–010 funded this work.
Languageen
PublisherSpringer Nature
SubjectCentral Serous Retinopathy
Data Augmentation
Deep Learning
Fundus Images
Optical Coherence Tomography Images
TitleDetection of central serous retinopathy using deep learning through retinal images
TypeArticle
Pagination21369-21396
Issue Number7
Volume Number83
ESSN1573-7721
dc.accessType Open Access


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