A Review on Automatic Glaucoma Detection in Retinal Fundus Images

Shahistha,; Vaidehi, K.; Srilatha, J.

doi:10.1007/978-981-15-1097-7_41

Cited by 1 publication

(2 citation statements)

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“…4 At its early stages, glaucoma causes no change in vision or feeling of pain despite the onset of disease symptoms. 5 At advanced stages, the progressive damaging of the optic nerve causes the glaucoma patient to start losing peripheral site, which if left untreated can advance to irreversible blindness. So far, there is no definite cure for vision loss caused by glaucoma.…”

Section: Introductionmentioning

confidence: 99%

“…IOP causes the gradual damaging of the optic nerve, which is responsible for carrying the signals from the eye to the brain for vision, leading to severe visual impairments 4 . At its early stages, glaucoma causes no change in vision or feeling of pain despite the onset of disease symptoms 5 . At advanced stages, the progressive damaging of the optic nerve causes the glaucoma patient to start losing peripheral site, which if left untreated can advance to irreversible blindness.…”

Section: Introductionmentioning

confidence: 99%

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TWEEC: Computer‐aided glaucoma diagnosis from retinal images using deep learning techniques

Abdel-Hamid

2021

Int J Imaging Syst Tech

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A novel two-branched deep convolutional (TWEEC) network is developed for computer-aided glaucoma diagnosis. The TWEEC network is designed to simultaneously extract anatomical information related to the optic disc and surrounding blood vessels which are the retinal structures most affected by glaucoma progression. The spatial retinal images and wavelet approximation subbands are compared as inputs to the proposed network. TWEEC's performance is compared to three implemented convolutional networks, one of which employs transfer learning. Experiments showed that the introduced TWEEC network achieved accuracies of 98.78% and 96.34% for the spatial and wavelet inputs, respectively, by that outperforming the other three deep networks by 8-15%. This work paves the way for the development of efficient deep learning based computer-aided glaucoma diagnosis tools. Moreover, the present study illustrates that considering specific wavelet subbands for the training of convolutional networks can result in reliable performance with the advantage of reduced overall network training time.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%