Correction to: Two-stage framework for optic disc localization and glaucoma classification in retinal fundus images using deep learning

Bajwa, Muhammad Naseer; Malik, Muhammad Imran; Siddiqui, Shoaib Ahmed; Dengel, Andreas; Neumeier, Wolfgang; Ahmed, Sheraz

doi:10.1186/s12911-019-0876-y

Cited by 20 publications

(8 citation statements)

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“…35 In the above discussed papers, it is evident that none of the techniques use erroneous images for training and testing. Despite the fact that extraction of handcrafted features is reduced in various papers, 26,[28][29][30]…”

Section: Transfer Learning Using Convolution Neural Network Modelsmentioning

confidence: 99%

“…In the above discussed papers, it is evident that none of the techniques use erroneous images for training and testing. Despite the fact that extraction of handcrafted features is reduced in various papers, 26,28–30 yet there are no erroneous images used for training and testing. The neural networks were trained, and region of interest (ROI) was detected to discriminate normal and glaucomatous images, but this process took a lot of computational time for classifying.…”

Section: Literature Reviewmentioning

confidence: 99%

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Deep learning based Glaucoma Network Classification (GNC) using retinal images

Kiyani,

Shehryar,

Khalid

et al. 2023

Int J Imaging Syst Tech

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The proposed deep learning framework for glaucoma classification addresses critical challenges of limited data and computational costs. Employing data augmentation and normalization techniques, the three‐stage model, utilizing InceptionV3 and ResNet50, achieves high training (99.3% ‐ 99.8%) and testing accuracy (91.6% ‐ 92.12%) on a dataset comprising 16,328 images from fused public datasets. This outperforms existing automated models. The approach leverages transfer learning and convolutional neural networks, showcasing its potential for accurate and timely glaucoma diagnosis. However, ongoing validation on diverse datasets and ethical considerations regarding fairness and transparency in medical applications remain essential. The model's reliability suggests its promising role in aiding early glaucoma detection, potentially averting irreversible vision impairment.

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Section: Transfer Learning Using Convolution Neural Network Modelsmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Deep learning based Glaucoma Network Classification (GNC) using retinal images

Kiyani,

Shehryar,

Khalid

et al. 2023

Int J Imaging Syst Tech

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“…This approach claimed better accuracy against on DENet ORIGA-650, RIM-ONE and DRISHTI-GS dataset. Bajwa et al [5] proposed a two-stage framework for classification of glaucoma from fundus images. First stage uses regions with convolutional neural network for localization and second stage uses CNN for classification of glaucoma.…”

Section: Related Workmentioning

confidence: 99%

“…Dice coefficient of two images P and Q can be seen as the harmonic mean of precision and recall, which are expressed in eq. ( 4) and (5).…”

Section: Evaluation Metricsmentioning

confidence: 99%

Improved optic disc and cup segmentation in Glaucomatic images using deep learning architecture

Mangipudi

Pandey

Choudhary

2021

Multimed Tools Appl

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Glaucoma is an ailment causing permanent vision loss but can be prevented through the early detection. Optic disc to cup ratio is one of the key factors for glaucoma diagnosis. But accurate segmentation of disc and cup is still a challenge. To mitigate this challenge, an effective system for optic disc and cup segmentation using deep learning architecture is presented in this paper. Modified Groundtruth is utilized to train the proposed model. It works as fused segmentation marking by multiple experts that helps in improving the performance of the system. Extensive computer simulations are conducted to test the efficiency of the proposed system. For the implementation three standard benchmark datasets such as DRISHTI-GS, DRIONS-DB and RIM-ONE v3 are used. The performance of the proposed system is validated against the state-of-the-art methods. Results indicate an average overlapping score of 96.62%, 96.15% and 98.42% respectively for optic disc segmentation and an average overlapping score of 94.41% is achieved on DRISHTI-GS which is significant for optic cup segmentation.

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“…These classification methods provide activation maps that allow the Usually, glaucoma is diagnosed on the basis of the patient's medical history, measures of intraocular pressure (IOP), a visual field loss test and manual evaluation of the optic disc (OD) using ophthalmoscopy to examine the shape and colour of the optic nerve. The examination of the OD is important since glaucoma begins to form a cavity and develops an abnormal depression/excavation at the front of the nerve head, called the optic cup, which, in advanced stages, facilitates the progression of glaucoma, blocking the OD (Figure 2) [6][7][8]. Usually, glaucoma is diagnosed on the basis of the patient's medical history, measures of intraocular pressure (IOP), a visual field loss test and manual evaluation of the optic disc (OD) using ophthalmoscopy to examine the shape and colour of the optic nerve.…”

Section: Introductionmentioning

confidence: 99%

Evaluations of Deep Learning Approaches for Glaucoma Screening Using Retinal Images from Mobile Device

Neto

Camara

Cunha

2022

Sensors

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Glaucoma is a silent disease that leads to vision loss or irreversible blindness. Current deep learning methods can help glaucoma screening by extending it to larger populations using retinal images. Low-cost lenses attached to mobile devices can increase the frequency of screening and alert patients earlier for a more thorough evaluation. This work explored and compared the performance of classification and segmentation methods for glaucoma screening with retinal images acquired by both retinography and mobile devices. The goal was to verify the results of these methods and see if similar results could be achieved using images captured by mobile devices. The used classification methods were the Xception, ResNet152 V2 and the Inception ResNet V2 models. The models’ activation maps were produced and analysed to support glaucoma classifier predictions. In clinical practice, glaucoma assessment is commonly based on the cup-to-disc ratio (CDR) criterion, a frequent indicator used by specialists. For this reason, additionally, the U-Net architecture was used with the Inception ResNet V2 and Inception V3 models as the backbone to segment and estimate CDR. For both tasks, the performance of the models reached close to that of state-of-the-art methods, and the classification method applied to a low-quality private dataset illustrates the advantage of using cheaper lenses.

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Correction to: Two-stage framework for optic disc localization and glaucoma classification in retinal fundus images using deep learning

Cited by 20 publications

References 1 publication

Deep learning based Glaucoma Network Classification (GNC) using retinal images

Deep learning based Glaucoma Network Classification (GNC) using retinal images

Improved optic disc and cup segmentation in Glaucomatic images using deep learning architecture

Evaluations of Deep Learning Approaches for Glaucoma Screening Using Retinal Images from Mobile Device

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