Automated Glaucoma Screening from Retinal Fundus Image Using Deep Learning

Phasuk, Siriporn; Poopresert, P.; Yaemsuk, A.; Suvannachart, Pukkapol; Itthipanichpong, Rath; Chansangpetch, Sunee; Manassakorn, Anita; Tantisevi, Visanee; Rojanapongpun, Prin; Tantibundhit, Charturong

doi:10.1109/embc.2019.8857136

Cited by 41 publications

(17 citation statements)

References 18 publications

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“…The burden of glaucoma care will continue to grow, and early diagnosis remains a much-needed strategy. AI could enhance access to large-scale screening and will improve medical support in low-resource countries through refined automated strategies for clinical diagnosis [35][36][37].…”

Section: Discussionmentioning

confidence: 99%

Analysis of international publication trends in artificial intelligence in ophthalmology

Boudry

Hajj

Arnould

et al. 2022

Graefes Arch Clin Exp Ophthalmol

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HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L'archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

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Section: Discussionmentioning

confidence: 99%

Analysis of international publication trends in artificial intelligence in ophthalmology

Boudry

Hajj

Arnould

et al. 2022

Graefes Arch Clin Exp Ophthalmol

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“…7 Accurate clinical detection of glaucoma requires proficient glaucoma specialists with a high level of expertise and training who are, in most cases, not abundantly available. 8 Recent advancements in artificial intelligence along with significant improvements in the retinal imaging technologies have paved the way for the development of reliable and inexpensive computer-aided glaucoma diagnosis tools. Computer-aided diagnosis can assist clinicians in their primary tests based on which only the suspicious cases would be further referred to a glaucoma specialist for additional manual examinations, final confirmation, and necessary treatment.…”

Section: Introductionmentioning

confidence: 99%

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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“…The authors of [75] used several retinal fundus images available in ORIGA, RIM-One-r3, and DRISHTI-GS databases to fuse the results of different deep learning networks to classify glaucoma evolution. It shows that the results are promising, reporting an AUC of 94%.…”

Section: Glaucoma Screeningmentioning

confidence: 99%

Literature Review on Artificial Intelligence Methods for Glaucoma Screening, Segmentation, and Classification

et al. 2022

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Artificial intelligence techniques are now being applied in different medical solutions ranging from disease screening to activity recognition and computer-aided diagnosis. The combination of computer science methods and medical knowledge facilitates and improves the accuracy of the different processes and tools. Inspired by these advances, this paper performs a literature review focused on state-of-the-art glaucoma screening, segmentation, and classification based on images of the papilla and excavation using deep learning techniques. These techniques have been shown to have high sensitivity and specificity in glaucoma screening based on papilla and excavation images. The automatic segmentation of the contours of the optic disc and the excavation then allows the identification and assessment of the glaucomatous disease’s progression. As a result, we verified whether deep learning techniques may be helpful in performing accurate and low-cost measurements related to glaucoma, which may promote patient empowerment and help medical doctors better monitor patients.

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Automated Glaucoma Screening from Retinal Fundus Image Using Deep Learning

Cited by 41 publications

References 18 publications

Analysis of international publication trends in artificial intelligence in ophthalmology

Analysis of international publication trends in artificial intelligence in ophthalmology

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

Literature Review on Artificial Intelligence Methods for Glaucoma Screening, Segmentation, and Classification

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