Automated diagnosis of diabetic retinopathy and glaucoma using fundus and OCT images
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TLDR
The proposed system for the automated diagnosis of diabetic retinopathy and glaucoma using fundus and optical coherence tomography (OCT) images is accurate, reliable and robust and can be realized.Abstract:
We describe a system for the automated diagnosis of diabetic retinopathy and glaucoma using fundus and optical coherence tomography (OCT) images. Automatic screening will help the doctors to quickly identify the condition of the patient in a more accurate way. The macular abnormalities caused due to diabetic retinopathy can be detected by applying morphological operations, filters and thresholds on the fundus images of the patient. Early detection of glaucoma is done by estimating the Retinal Nerve Fiber Layer (RNFL) thickness from the OCT images of the patient. The RNFL thickness estimation involves the use of active contours based deformable snake algorithm for segmentation of the anterior and posterior boundaries of the retinal nerve fiber layer. The algorithm was tested on a set of 89 fundus images of which 85 were found to have at least mild retinopathy and OCT images of 31 patients out of which 13 were found to be glaucomatous. The accuracy for optical disk detection is found to be 97.75%. The proposed system therefore is accurate, reliable and robust and can be realized.read more
Citations
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Journal ArticleDOI
A computer-aided diagnostic system for detecting diabetic retinopathy in optical coherence tomography images.
Ahmed ElTanboly,Ahmed ElTanboly,Marwa Ismail,Ahmed Shalaby,A. Switala,Ayman El-Baz,Shlomit Schaal,Georgy Gimel'farb,M. S. El-Azab +8 more
TL;DR: Both the quantitative and visual assessments confirmed the high accuracy of the proposed computer‐assisted diagnostic system for early DR detection using the OCT retinal images.
Journal ArticleDOI
A convolutional neural network for the screening and staging of diabetic retinopathy
Mohamed Shaban,Zeliha Ogur,Ali Mahmoud,Andrew E. Switala,Ahmed Shalaby,Hadil Abu Khalifeh,Mohammed Ghazal,Luay Fraiwan,Guruprasad A. Giridharan,Harpal Sandhu,Ayman El-Baz +10 more
TL;DR: The proposed approach is considerably accurate in objectively diagnosing and grading diabetic retinopathy, which obviates the need for a retina specialist and expands access to retinal care.
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Fundus image classification methods for the detection of glaucoma: A review
TL;DR: This article audits a few division and segmentation methodologies that are exceptionally useful for recognizable proof, identification, and diagnosis of glaucoma.
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Deep learning based early stage diabetic retinopathy detection using optical coherence tomography
TL;DR: This work developed and evaluated a novel deep network – OCTD_Net, for early-stage DR detection and suggests that grade 1 DR patients present with significant changes in the thickness and reflection of certain retinal layers, however, grade 0 DR patients do not have such significant changes.
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Cross-Examination for Angle-Closure Glaucoma Feature Detection
Swamidoss Issac Niwas,Weisi Lin,Chee Keong Kwoh,C.-C. Jay Kuo,Chelvin C A Sng,Maria Cecilia Aquino,Paul T K Chew +6 more
TL;DR: The overall accuracy has shown that the usefulness of redundant features by L-score method in improved ACG diagnosis compared to minimum redundancy features by MRMR method.
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