Detection of Glaucoma Using Image Processing Techniques: A Critique

Kumar, B. Naveen; Chauhan, R. P.; Dahiya, Nidhi

doi:10.1080/08820538.2016.1229801

Cited by 8 publications

(9 citation statements)

References 8 publications

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“…In glaucoma screening, experts analyze clinical data from optic nerve head analysis, biomarkers, and ancillary exams based on their experiences [ 8 ]. However, studies show low agreement between examiners in evaluating stereo photographs and only moderate reproducibility to detect glaucomatous features in the optic disc [ 3 ].…”

Section: Some Approaches To Medical Practice In Glaucomamentioning

confidence: 99%

Clinical Perspectives on the Use of Computer Vision in Glaucoma Screening

Camara,

Cunha

2024

Medicina

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Glaucoma is one of the leading causes of irreversible blindness in the world. Early diagnosis and treatment increase the chances of preserving vision. However, despite advances in techniques for the functional and structural assessment of the retina, specialists still encounter many challenges, in part due to the different presentations of the standard optic nerve head (ONH) in the population, the lack of explicit references that define the limits of glaucomatous optic neuropathy (GON), specialist experience, and the quality of patients’ responses to some ancillary exams. Computer vision uses deep learning (DL) methodologies, successfully applied to assist in the diagnosis and progression of GON, with the potential to provide objective references for classification, avoiding possible biases in experts’ decisions. To this end, studies have used color fundus photographs (CFPs), functional exams such as visual field (VF), and structural exams such as optical coherence tomography (OCT). However, it is still necessary to know the minimum limits of detection of GON characteristics performed through these methodologies. This study analyzes the use of deep learning (DL) methodologies in the various stages of glaucoma screening compared to the clinic to reduce the costs of GON assessment and the work carried out by specialists, to improve the speed of diagnosis, and to homogenize opinions. It concludes that the DL methodologies used in automated glaucoma screening can bring more robust results closer to reality.

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Section: Some Approaches To Medical Practice In Glaucomamentioning

confidence: 99%

Clinical Perspectives on the Use of Computer Vision in Glaucoma Screening

Camara,

Cunha

2024

Medicina

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“…In the case of extreme intraocular pressure levels, headache, sudden eye pain, impaired vision, or the formation of halos around lights might occur. Loss of vision Eye redness Hazy eyes (specifically in infants) Vomiting or nausea Vision narrowing (tunnel vision) [ 4 ] …”

Section: Introductionmentioning

confidence: 99%

“…The forms vary between different ethnicities in intensity, complexity, and occurrence. Open-angle and angle-closure glaucoma are the two major forms of glaucoma [ 4 ]. Figure 2 is shown in optic nerve head structure.…”

Section: Introductionmentioning

confidence: 99%

Segmentation and Classification of Glaucoma Using U-Net with Deep Learning Model

Sinthuja

Raja

et al. 2022

Journal of Healthcare Engineering

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Glaucoma is the second most common cause for blindness around the world and the third most common in Europe and the USA. Around 78 million people are presently living with glaucoma (2020). It is expected that 111.8 million people will have glaucoma by the year 2040. 90% of glaucoma is undetected in developing nations. It is essential to develop a glaucoma detection system for early diagnosis. In this research, early prediction of glaucoma using deep learning technique is proposed. In this proposed deep learning model, the ORIGA dataset is used for the evaluation of glaucoma images. The U-Net architecture based on deep learning algorithm is implemented for optic cup segmentation and a pretrained transfer learning model; DenseNet-201 is used for feature extraction along with deep convolution neural network (DCNN). The DCNN approach is used for the classification, where the final results will be representing whether the glaucoma infected or not. The primary objective of this research is to detect the glaucoma using the retinal fundus images, which can be useful to determine if the patient was affected by glaucoma or not. The result of this model can be positive or negative based on the outcome detected as infected by glaucoma or not. The model is evaluated using parameters such as accuracy, precision, recall, specificity, and F-measure. Also, a comparative analysis is conducted for the validation of the model proposed. The output is compared to other current deep learning models used for CNN classification, such as VGG-19, Inception ResNet, ResNet 152v2, and DenseNet-169. The proposed model achieved 98.82% accuracy in training and 96.90% in testing. Overall, the performance of the proposed model is better in all the analysis.

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“…Also, in the field of ophthalmology, lots of manuscripts showed the potential of AI in automated applications for classification, segmentation, prediction of various eye disease diagnosis and detection. After the analysis of different researchers views on the ophthalmology-, Diabetic Retinopathy (DR) [10][11], Age-related Macular Degeneration (AMD) [12], Glaucoma [13], Diabetic Macular Edema (DME) [14] etc. eye diseases are the leading causes of blindness in the mostly aging populaces.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning Based Analysis of Ophthalmology: A Systematic Review

Kumar¹,

Kumar²,

Gupta³

2021

EAI Endorsed Trans Perv Health Tech

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INTRODUCTION: Diagnosis of medical-related problems is the biggest issue in every era. In past decades, due to less number of technologies and equipment's it was challenging to diagnose it. As time passes, technologies i.e. Artificial Intelligence (AI) grow and became popular in the medical field especially in ophthalmology at proliferating rate. But, still, many of the diseases are diagnose manually. (i.e., eye disease disorder) which is time consuming, expensive, and tedious task. Existing prediction systems can resolve medical issues such as ocular disorder but the accuracy of prediction is very less. OBJECTIVES: This study gives a brief overview of the analysis of traditional systems with modern approaches. Further, this study highlights the different allied techniques and impact of transfer learning on ophthalmology for the detection of various eye diseases i.e., Diabetic Retinopathy (DR), Age-related Macular Degeneration (AMD), Glaucoma, etc. METHODS: AI and Machine Learning Technique are used to conduct this research for analysis. RESULTS: The result of this paper concludes AI with allied techniques may reshape and revolutionize the medical community especially in the area of ophthalmology. CONCLUSION: This paper presented a comprehensive review of AI with allied techniques in ophthalmology. In ML and DL-based approaches, CNN provides the most promising results.

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Detection of Glaucoma Using Image Processing Techniques: A Critique

Abstract: The techniques mentioned in the present review have certain advantages and disadvantages. Based on this study, one can determine which technique provides an optimum result.

Cited by 8 publications

References 8 publications

Clinical Perspectives on the Use of Computer Vision in Glaucoma Screening

Clinical Perspectives on the Use of Computer Vision in Glaucoma Screening

Segmentation and Classification of Glaucoma Using U-Net with Deep Learning Model

Deep Learning Based Analysis of Ophthalmology: A Systematic Review

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