Image Processing Based Automated Glaucoma Detection Techniques and Role of De-Noising: A Technical Survey

Sahu, Sima; Singh, Harsh Vikram; Kumar, Basant; Singh, Amit Kumar; Kumar, Parvinder

doi:10.1007/978-3-030-15887-3_16

Cited by 8 publications

(5 citation statements)

References 54 publications

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“…Denoising is important in the area of medical imaging. In general, optical coherence tomography (OCT) images are corrupted by speckle noise (Sahu et al, 2019). Speckle noise is a multiplicative noise.…”

Section: Methodsmentioning

confidence: 99%

Automated glaucoma detection from fundus images using wavelet-based denoising and machine learning

Khan¹,

Choubey²,

Choubey³

et al. 2021

Concurrent Engineering

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Glaucoma is a domineering and irretrievable neurodegenerative eye disease produced by the optical nerve head owed to extended intra-ocular stress inside the eye. Recognition of glaucoma is an essential job for ophthalmologists. In this paper, we propose a methodology to classify fundus images into normal and glaucoma categories. The proposed approach makes use of image denoising of digital fundus images by utilizing a non-Gaussian bivariate probability distribution function to model the statistics of wavelet coefficients of glaucoma images. The traditional image features were extracted followed by the popular feature selection algorithm. The selected features are then fed to the least square support vector machine classifier employing various kernel functions. The comparison result shows that the proposed approach offers maximum classification accuracy of nearly 91.22% over the existing best approaches.

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Section: Methodsmentioning

confidence: 99%

Automated glaucoma detection from fundus images using wavelet-based denoising and machine learning

Khan¹,

Choubey²,

Choubey³

et al. 2021

Concurrent Engineering

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show abstract

“…In the advanced stage, the nerve fibers are damaged and the optic disc region is fully cupped leading to loss of vision. Additionally, macular degeneration, a degenerative eye disease affecting the macula responsible for sharp visual acuity, can also be present [7]. By utilizing the appropriate image processing algorithm, it is possible to obtain accurate and automated measurements of both the optic disc and optic cup diameters from a fundus image.…”

Section: Figure 1 Progressive Glaucomamentioning

confidence: 99%

Genetically Optimized Neural Network for Early Detection of Glaucoma and Cardiovascular Disease Risk Prediction

Muthu,

Devadoss

2023

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Glaucoma, a major ocular disease, often culminates in irreversible blindness if undiagnosed. Characterized by optic nerve fiber degeneration, it instigates structural changes to the optic nerve, thus precipitating vision loss. Despite being symptomless, early detection is imperative to prevent further visual impairment. The disease's inception is attributed to increased intraocular pressure, a condition influenced by blood pressure. Notably, the eye and heart share parallel characteristics, making glaucoma an early indicator of potential cardiac conditions. Increased blood pressure frequently accompanies Diabetes mellitus -a common complication exacerbating cardiac health and fostering the development of cardiovascular diseases. Leveraging computational technologies allows for the early-stage identification of glaucoma. The utilization of deep learning approaches and pruning techniques has yielded significant outcomes in detecting glaucoma-related abnormalities accurately. Pruning, a strategy implemented to eliminate redundant parameters while preserving optimal performance, is particularly beneficial. This study introduces a Genetically Optimized Neural Network (GONN) incorporating wavelet transformation for the detection of glaucoma, thereby assisting in diabetes and heart disease risk identification. Experimental results demonstrate that the GONN method outperforms conventional methods such as Artificial Neural Networks (ANN), Naï ve Bayes, multilayer perceptron, ensemble methods, K-Nearest Neighbor (KNN), and decision trees. Notably, the GONN technique achieves an impressive accuracy of 95%, an F1 score of 92%, and an Area Under the Curve (AUC) of 98.92%. This study's findings underscore the potential of the GONN technique in accurately identifying glaucoma, thereby aiding in early diabetes and heart disease risk prediction. The results demonstrate that the GONN approach is a viable tool for clinical practice, with potential implications for improved patient outcomes and healthcare efficiency.

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“…Various algorithms in the fields of enhancement [7], segmentation [8][9][10], and classification [11,12] of fundus images have been developed based on merging image processing and deep learning principles. Deep learning algorithms can be sufficiently trained and are less prone to overfitting for datasets with more images, and test results accuracy can exceed 95%.…”

Section: Introductionmentioning

confidence: 99%

Multi-Label Fundus Image Classification Using Attention Mechanisms and Feature Fusion

Yang

et al. 2022

Micromachines

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Fundus diseases can cause irreversible vision loss in both eyes if not diagnosed and treated immediately. Due to the complexity of fundus diseases, the probability of fundus images containing two or more diseases is extremely high, while existing deep learning-based fundus image classification algorithms have low diagnostic accuracy in multi-labeled fundus images. In this paper, a multi-label classification of fundus disease with binocular fundus images is presented, using a neural network algorithm model based on attention mechanisms and feature fusion. The algorithm highlights detailed features in binocular fundus images, and then feeds them into a ResNet50 network with attention mechanisms to extract fundus image lesion features. The model obtains global features of binocular images through feature fusion and uses Softmax to classify multi-label fundus images. The ODIR binocular fundus image dataset was used to evaluate the network classification performance and conduct ablation experiments. The model’s backend is the Tensorflow framework. Through experiments on the test images, this method achieved accuracy, precision, recall, and F1 values of 94.23%, 99.09%, 99.23%, and 99.16%, respectively.

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Image Processing Based Automated Glaucoma Detection Techniques and Role of De-Noising: A Technical Survey

Cited by 8 publications

References 54 publications

Automated glaucoma detection from fundus images using wavelet-based denoising and machine learning

Automated glaucoma detection from fundus images using wavelet-based denoising and machine learning

Genetically Optimized Neural Network for Early Detection of Glaucoma and Cardiovascular Disease Risk Prediction

Multi-Label Fundus Image Classification Using Attention Mechanisms and Feature Fusion

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