Retinal Fundus Image for Glaucoma Detection: A Review and Study

Kanse, Shilpa Sameer; Yadav, D. M.

doi:10.1515/jisys-2016-0258

Cited by 25 publications

(6 citation statements)

References 39 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Many researchers in the eld of glaucoma detection have faced limitations due to working with small datasets and fundus images from private sources, which often lack real-time variations in image quality. This situation hinders the robustness and generalizability of the developed models [21,22]. To address this challenge, it is crucial to develop a model that can effectively handle images acquired under different environmental conditions.…”

Section: Introductionmentioning

confidence: 99%

Enhancing Eye Diseases Diagnosis through Transfer Learning: Study of Deep Convolutional Neural Networks for Accurate Classification of Glaucoma and Diabetic Retinopathy from Healthy Eye Using Fundus Images

Tajerian

Keshtkar

Almasi‐Hashiani

et al. 2023

Preprint

View full text Add to dashboard Cite

Fundoscopy, or ophthalmoscopy, is a medical procedure used to examine the inner structures of the eye. Fundoscopic images are valuable resources for developing artificial intelligence systems to aid in the diagnosis and management of eye conditions. This paper focuses on enhancing the robustness and generalizability of machine learning-based retinal image classification systems. A diverse and large-scale dataset of approximately 100,000 retinal images was utilized, along with a complex machine learning model. The study employed multiple datasets, including Kim's Eye Hospital, Drishti-GS1, DR HAGIS, APTOS 2019 Blindness Detection, ACRIMA, and Diabetic Retinopathy Detection 2015, to evaluate the performance of the model. Preprocessing techniques, including contrast enhancement and image resizing, were applied to prepare the dataset. The DenseNet121 model, which addresses the vanishing gradient problem, was used for transfer learning. The model created in this study can classify fundus images to three classes of Diabetic retinopathy, Glaucoma, and healthy eye with an average accuracy of 84.78%, a precision of 84.75%, and a recall of 84.76%. Although by training a model on a mild DR omitted dataset, these metrics increased significantly to an accuracy of 97.97%, a precision of 97.97%, and a recall of 97.96%. Results demonstrated that excluding mild diabetic retinopathy cases from the dataset significantly improved the model's performance.

show abstract

Section: Introductionmentioning

confidence: 99%

Enhancing Eye Diseases Diagnosis through Transfer Learning: Study of Deep Convolutional Neural Networks for Accurate Classification of Glaucoma and Diabetic Retinopathy from Healthy Eye Using Fundus Images

Tajerian

Keshtkar

Almasi‐Hashiani

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…An ophthalmologist can directly examine the eye with an ophthalmoscope or can examine a fundus image capture with a fundus camera, as can be seen in Figure 1. The examination of these fundus images is important because the ophthalmologist can record indicators and parameters related to cupping to detect glaucoma, such as disc diameter, the thickness of the neuroretinal rim (decreasing in the order inferior (I) > superior (S) > nasal (N) > temporal (T) (ISNT rule)), peripapillary atrophy, notching and cup-to-disc ratio (CDR), with this last indicator being the most used measurement by specialists [3][4][5]. 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

View full text Add to dashboard Cite

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.

show abstract

“…Retinal imaging analysis plays a fundamental role in the diagnosis of ophthalmic diseases. In that context, image segmentation processing have been applied in retinal fundus images for the early detection of glaucoma [ 11 ], diabetic retinopathy [ 12 ], or age-related macular degeneration [ 13 ]. If the angular distribution of the IS is wide enough, the sharpness, contrast, and image resolution will be decreased, making the features extraction harder even with advanced post-processing techniques.…”

Section: Introductionmentioning

confidence: 99%

Iterative-Trained Semi-Blind Deconvolution Algorithm to Compensate Straylight in Retinal Images

et al. 2021

View full text Add to dashboard Cite

The optical quality of an image depends on both the optical properties of the imaging system and the physical properties of the medium in which the light travels from the object to the final imaging sensor. The analysis of the point spread function of the optical system is an objective way to quantify the image degradation. In retinal imaging, the presence of corneal or cristalline lens opacifications spread the light at wide angular distributions. If the mathematical operator that degrades the image is known, the image can be restored through deconvolution methods. In the particular case of retinal imaging, this operator may be unknown (or partially) due to the presence of cataracts, corneal edema, or vitreous opacification. In those cases, blind deconvolution theory provides useful results to restore important spatial information of the image. In this work, a new semi-blind deconvolution method has been developed by training an iterative process with the Glare Spread Function kernel based on the Richardson-Lucy deconvolution algorithm to compensate a veiling glare effect in retinal images due to intraocular straylight. The method was first tested with simulated retinal images generated from a straylight eye model and applied to a real retinal image dataset composed of healthy subjects and patients with glaucoma and diabetic retinopathy. Results showed the capacity of the algorithm to detect and compensate the veiling glare degradation and improving the image sharpness up to 1000% in the case of healthy subjects and up to 700% in the pathological retinal images. This image quality improvement allows performing image segmentation processing with restored hidden spatial information after deconvolution.

show abstract

Retinal Fundus Image for Glaucoma Detection: A Review and Study

Cited by 25 publications

References 39 publications

Enhancing Eye Diseases Diagnosis through Transfer Learning: Study of Deep Convolutional Neural Networks for Accurate Classification of Glaucoma and Diabetic Retinopathy from Healthy Eye Using Fundus Images

Enhancing Eye Diseases Diagnosis through Transfer Learning: Study of Deep Convolutional Neural Networks for Accurate Classification of Glaucoma and Diabetic Retinopathy from Healthy Eye Using Fundus Images

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

Iterative-Trained Semi-Blind Deconvolution Algorithm to Compensate Straylight in Retinal Images

Contact Info

Product

Resources

About