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المؤلفHassan, Syed Ale
المؤلفAkbar, Shahzad
المؤلفKhan, Habib Ullah
تاريخ الإتاحة2024-04-30T11:12:14Z
تاريخ النشر2023-08-02
اسم المنشورMultimedia Tools and Applications
المعرّفhttp://dx.doi.org/10.1007/s11042-023-16206-y
الاقتباسHassan, 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.
الرقم المعياري الدولي للكتاب1380-7501
معرّف المصادر الموحدhttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85166567863&origin=inward
معرّف المصادر الموحدhttp://hdl.handle.net/10576/54519
الملخصThe 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.
راعي المشروعThis 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.
اللغةen
الناشرSpringer Nature
الموضوعCentral Serous Retinopathy
Data Augmentation
Deep Learning
Fundus Images
Optical Coherence Tomography Images
العنوانDetection of central serous retinopathy using deep learning through retinal images
النوعArticle
الصفحات21369-21396
رقم العدد7
رقم المجلد83
ESSN1573-7721
dc.accessType Open Access


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