Segmentation and Detection of Vascular Bifurcations and Crossings in Retinal Images

2018 ◽  
pp. 432-443
Author(s):  
Maria Aguiar ◽  
Felipe Castano ◽  
Maria Trujillo
Keyword(s):  
Retinal Images

A Comparative Study of Different Blood Vessel Detection on Retinal Images

2019 ◽  
Vol 11 (4) ◽  
Author(s):  
Wan Azani Mustafa ◽  
◽  
Ahmad Syauqi Mahmud ◽  
Muhammad Zaid Aihsan ◽  
M. Saifizi ◽  
...  
Keyword(s):  
Blood Vessel ◽  
Comparative Study ◽  
Retinal Images ◽  
Blood Vessel Detection ◽  
Vessel Detection

scholarly journals Screening Using Compressed Digital Retinal Images Successfully Identifies Retinopathy

Diabetes Care ◽  
2003 ◽  
Vol 26 (1) ◽  
pp. 247-247 ◽  
Author(s):  
G. P. Leese ◽  
A. Ellingford ◽  
A. D. Morris ◽  
J. D. Ellis ◽  
S. Cunningham
Keyword(s):  
Retinal Images

Automatic detection of papilledema through fundus retinal images using deep learning

2021 ◽  
Author(s):  
Tanzila Saba ◽  
Shahzad Akbar ◽  
Hoshang Kolivand ◽  
Saeed Ali Bahaj
Keyword(s):  
Deep Learning ◽  
Automatic Detection ◽  
Retinal Images

scholarly journals Development of a Fundus Image-Based Deep Learning Diagnostic Tool for Various Retinal Diseases

2021 ◽  
Vol 11 (5) ◽  
pp. 321
Author(s):  
Kyoung Min Kim ◽  
Tae-Young Heo ◽  
Aesul Kim ◽  
Joohee Kim ◽  
Kyu Jin Han ◽  
...  
Keyword(s):  
Network Models ◽  
Retinal Disease ◽  
Age Related Macular Degeneration ◽  
Diagnostic Tools ◽  
Retinal Images ◽  
Dense Layer ◽  
Retinal Diseases ◽  
Fundus Image ◽  
Neural Network Models ◽  
Age Related

Artificial intelligence (AI)-based diagnostic tools have been accepted in ophthalmology. The use of retinal images, such as fundus photographs, is a promising approach for the development of AI-based diagnostic platforms. Retinal pathologies usually occur in a broad spectrum of eye diseases, including neovascular or dry age-related macular degeneration, epiretinal membrane, rhegmatogenous retinal detachment, retinitis pigmentosa, macular hole, retinal vein occlusions, and diabetic retinopathy. Here, we report a fundus image-based AI model for differential diagnosis of retinal diseases. We classified retinal images with three convolutional neural network models: ResNet50, VGG19, and Inception v3. Furthermore, the performance of several dense (fully connected) layers was compared. The prediction accuracy for diagnosis of nine classes of eight retinal diseases and normal control was 87.42% in the ResNet50 model, which added a dense layer with 128 nodes. Furthermore, our AI tool augments ophthalmologist’s performance in the diagnosis of retinal disease. These results suggested that the fundus image-based AI tool is applicable for the medical diagnosis process of retinal diseases.

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