Automatic Early Stage Glaucoma Detection Using Cascade Correlation Neural Network

Devasia, Thresiamma; Jacob, K. Poulose; Thomas, Tessamma

doi:10.1007/978-981-13-1921-1_64

Cited by 8 publications

(1 citation statement)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Deep CNNs have proven to be an efficient AI-based tool in identifying clinically significant features from retinal fundus images [28][29][30][31] . Deep CNNs trained on glaucomatous fundus images have achieved a varied performance ranging from 0.75 to 0.9 in sensitivity and 0.65 to 0.97 specificity 16,32,33 and these differences may be related to sample size. A recent study by Liu et al 34 , using a deep CNN architecture applied to 3,788 fundus photographs, showed a 87.9% in sensitivity and 96.5% in specificity in glaucomatous disc identification.…”

Section: Discussionmentioning

confidence: 99%

A combined convolutional and recurrent neural network for enhanced glaucoma detection

Gheisari

Shariflou

Phu

et al. 2021

Sci Rep

View full text Add to dashboard Cite

Glaucoma, a leading cause of blindness, is a multifaceted disease with several patho-physiological features manifesting in single fundus images (e.g., optic nerve cupping) as well as fundus videos (e.g., vascular pulsatility index). Current convolutional neural networks (CNNs) developed to detect glaucoma are all based on spatial features embedded in an image. We developed a combined CNN and recurrent neural network (RNN) that not only extracts the spatial features in a fundus image but also the temporal features embedded in a fundus video (i.e., sequential images). A total of 1810 fundus images and 295 fundus videos were used to train a CNN and a combined CNN and Long Short-Term Memory RNN. The combined CNN/RNN model reached an average F-measure of 96.2% in separating glaucoma from healthy eyes. In contrast, the base CNN model reached an average F-measure of only 79.2%. This proof-of-concept study demonstrates that extracting spatial and temporal features from fundus videos using a combined CNN and RNN, can markedly enhance the accuracy of glaucoma detection.

show abstract