Real-Time Glaucoma Detection from Digital Fundus Images using Self-ONNs

ECNet: An evolutionary convolutional network for automated glaucoma detection using fundus images

Biomedical Signal Processing and Control ◽

10.1016/j.bspc.2021.102559 ◽

2021 ◽

Vol 67 ◽

pp. 102559

Author(s):

Deepak Ranjan Nayak ◽

Dibyasundar Das ◽

Banshidhar Majhi ◽

Sulatha V. Bhandary ◽

U. Rajendra Acharya

Keyword(s):

Fundus Images ◽

Convolutional Network ◽

Glaucoma Detection

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Faster R-CNN and DenseNet Regression for Glaucoma Detection in Retinal Fundus Images

2020 2nd International Conference on Computer and Information Sciences (ICCIS) ◽

10.1109/iccis49240.2020.9257680 ◽

2020 ◽

Author(s):

Manar Aljazaeri ◽

Yakoub Bazi ◽

Haidar AlMubarak ◽

Naif Alajlan

Keyword(s):

Fundus Images ◽

Glaucoma Detection ◽

Retinal Fundus Images ◽

Retinal Fundus

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Optic Disc and Cup Segmentation with Blood Vessel Removal from Fundus Images for Glaucoma Detection

2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) ◽

10.1109/embc.2018.8512400 ◽

2018 ◽

Cited By ~ 6

Author(s):

Yuming Jiang ◽

Hu Xia ◽

Yanwu Xu ◽

Jun Cheng ◽

Huazhu Fu ◽

...

Keyword(s):

Blood Vessel ◽

Optic Disc ◽

Fundus Images ◽

Glaucoma Detection

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Detection of Multi-Class Retinal Diseases Using Artificial Intelligence: An Expeditious Learning Using Deep CNN with Minimal Data

Biomedical & Pharmacology Journal ◽

10.13005/bpj/1788 ◽

2019 ◽

Vol 12 (3) ◽

pp. 1577-1586 ◽

Cited By ~ 2

Author(s):

Karthikeyan S. ◽

Sanjay Kumar P. ◽

R J Madhusudan Madhusudan ◽

S K Sundaramoorthy Sundaramoorthy ◽

P K Krishnan Namboori3

Keyword(s):

Real Time ◽

Retinal Vein Occlusion ◽

Central Retinal Vein Occlusion ◽

Research Work ◽

Diagnostic Strategy ◽

Fundus Images ◽

Vein Occlusion ◽

Deep Cnn ◽

The Right ◽

Central Retinal Vein

The health-related complications such as diabetes, macular degeneration, inflammatory conditions, ageing and fungal infections may cause damages to the retina and the macula of the eye, leading to permanent vision loss. The major diseases associated with retina are Arteriosclerotic retinopathy (AR), Central retinal vein occlusion (CRVO), Branch retinal artery occlusion (BRAO), Coat's disease (CD) and Hemi-Central Retinal Vein Occlusion (HRVO). The symptomatic variations among these disorders are relatively confusing so that a systematic diagnostic strategy is difficult to set in. Therefore, an early detection device is required that is capable of differentiating the various ophthalmic complications and thereby helping in providing the right treatment to the patient at the right time. In this research work, 'Deep Convolution Neural Networks (Deep CNN) based machine learning approach has been used for the detection of the twelve major retinal complications from the minimal set of fundus images. The model was further cross-validated with real-time fundus images. The model is found to be superior in its efficiency, specificity and ability to minimize the misclassification. The “multi-class retinal disease” model on further cross-validation with real-time fundus image of the gave an accuracy of 95.63 %, validation accuracy of 92.99 % and F1 score of 91.96 %. The multi-class model is found to be a theranostic clinical support system for the ophthalmologist for diagnosing different kinds of retinal problems, especially BRAO, BRVO, CRAO, CD, DR, HRVO, HP, HR, and CN.

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