Segmentation of Optic Cup and Disc for Diagnosis of Glaucoma on Retinal Fundus Images

Joshua, Afolabi O.; Nelwamondo, Fulufhelo V.; Mabuza-Hocquet, Gugulethu

doi:10.1109/robomech.2019.8704727

Cited by 20 publications

(5 citation statements)

References 12 publications

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“…In [11], OD and OC have been segmented simultaneously using a modified deep fully convolutional network (FCN)with preprocessing. In [40], segmentation was performed using an enhanced U-net architecture. For OC segmentation, images were cropped and scaled-down and then fed into a modified U-net convolutional network with more convolutional layers and fewer parameters.…”

Section: Related Workmentioning

confidence: 99%

CDED-Net: Joint Segmentation of Optic Disc and Optic Cup for Glaucoma Screening

et al. 2020

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Glaucoma is an eye disease that can cause loss of vision by damaging the optic nerve. It is the world's second leading cause of blindness after cataracts. Early diagnosis of glaucoma is a key to prevent permanent blindness as it has no noticeable symptoms in its early stages. Color fundus photography is used for examining the optic disc (OD) which is an important step in the diagnoses of glaucoma. This is done by estimating the cup-to-disc ratio (CDR). In this paper, we proposed a Cup Disc Encoder Decoder Network (CDED-Net) for the joint segmentation of optic disc (OD) and optic cup (OC). We have eradicated the preprocessing and post-processing steps to reduce the computational cost of the overall system. Segmentation of (OD) and OC is modeled as a semantic pixel-wise labeling problem. The model was trained on the DRISHTI-GS, RIM-ONE and REFUGE datasets. Experiments show that our CDED-Net system achieves state-of-the-art OD and OC segmentation results on these datasets.

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Section: Related Workmentioning

confidence: 99%

CDED-Net: Joint Segmentation of Optic Disc and Optic Cup for Glaucoma Screening

et al. 2020

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“…CNNs are popular because they require much lower pre-processing when compared to other image-classification algorithms. They have been employed in several image-processing tasks such as in detection of glaucoma [15,16], torsional evaluation of reinforced concrete beams [17], concrete crack detection [18], and other tasks.…”

Section: Related Workmentioning

confidence: 99%

On the Relative Impact of Optimizers on Convolutional Neural Networks with Varying Depth and Width for Image Classification

2022

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The continued increase in computing resources is one key factor that is allowing deep learning researchers to scale, design and train new and complex convolutional neural network (CNN) architectures in terms of varying width, depth, or both width and depth to improve performance for a variety of problems. The contributions of this study include an uncovering of how different optimization algorithms impact CNN architectural setups with variations in width, depth, and both width/depth. Specifically in this study, three different CNN architectural setups in combination with nine different optimization algorithms—namely SGD vanilla, with momentum, and with Nesterov momentum, RMSProp, ADAM, ADAGrad, ADADelta, ADAMax, and NADAM—are trained and evaluated using three publicly available benchmark image classification datasets. Through extensive experimentation, we analyze the output predictions of the different optimizers with the CNN architectures using accuracy, convergence speed, and loss function as performance metrics. Findings based on the overall results obtained across the three image classification datasets show that ADAM and NADAM achieved superior performances with wider and deeper/wider setups, respectively, while ADADelta was the worst performer, especially with the deeper CNN architectural setup.

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“…The use of U-Net architecture for optical cup segmentation was done by Joshua et al [10]. The authors improved U-Net Convolutional Neural Network to segment optic cup from retinal image.…”

Section: Related Workmentioning

confidence: 99%

Optic Cup Segmentation using U-Net Architecture on Retinal Fundus Image

Prastyo

Sumi²,

Nuraini³

2020

j.inf.tech.comp.eng

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Retinal fundus images are used by ophthalmologists to diagnose eye disease, such as glaucoma disease. The diagnosis of glaucoma is done by measuring changes in the cup-to-disc ratio. Segmenting the optic cup helps petrify ophthalmologists calculate the CDR of the retinal fundus image. This study proposed a deep learning approach using U-Net architecture to carry out segmentation task. This proposed method was evaluated on 650 color retinal fundus image. Then, U-Net was configured using 160 epochs, image input size = 128x128, Batch size = 32, optimizer = Adam, and loss function = Binary Cross Entropy. We employed the Dice Coefficient as the evaluator. Besides, the segmentation results were compared to the ground truth images. According to the experimental results, the performance of optic cup segmentation achieved 98.42% for the Dice coefficient and loss of 1,58%. These results implied that our proposed method succeeded in segmenting the optic cup on color retinal fundus images.

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Segmentation of Optic Cup and Disc for Diagnosis of Glaucoma on Retinal Fundus Images

Cited by 20 publications

References 12 publications

CDED-Net: Joint Segmentation of Optic Disc and Optic Cup for Glaucoma Screening

CDED-Net: Joint Segmentation of Optic Disc and Optic Cup for Glaucoma Screening

On the Relative Impact of Optimizers on Convolutional Neural Networks with Varying Depth and Width for Image Classification

Optic Cup Segmentation using U-Net Architecture on Retinal Fundus Image

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