Assessment of Disc Damage Likelihood Scale (DDLS) for Automated Glaucoma Diagnosis

Singh, Rana Uday; Gujral, Shruti

doi:10.1016/j.procs.2014.09.028

Cited by 8 publications

(3 citation statements)

References 7 publications

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“…Cheng et al started with acquisition of desired area and applied low pass filtering to extract edges from the image and two-dimensional median filtering to further smoothen it [31]. Rana et al used min, max and median filters for removal of blood vessels in red and green color spaces of input retinal image for segmentation of optic disc and cup [32]. Murugan et al used lightness component of Lab color space with bilateral filter for pre-processing the input image [33].…”

Section: Filtering Based Approaches [27-29]mentioning

confidence: 99%

Pre-processing of Retinal Images for Removal of Outliers

Thakur

Juneja

2020

Wireless Pers Commun

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Early diagnosis of diseases related with retina such as glaucoma is of utmost importance in current scenario as it is the second most prevailing cause of irreversible blindness over the world and is expected to increase further in near future. It is commonly diagnosed using retinal images which are acquired by digital fundus cameras. But the acquired images may be prone to certain outliers that create hindrance in diagnosis of glaucoma by tempering the accuracy. These outliers include retinal vessels, low contrast of images and uneven illumination that deteriorates the performance of disc and cup segmentation which are the key indicators to diagnose glaucoma. Thus, pre-processing of retinal images to remove outliers plays a significant role in diagnosis. This paper presents an approach for pre-processing the retinal fundus image followed by its comparison with state of the art. Based on the experimental analysis the performance of the proposed approach is found to be better than the state of the art based on the analysis using metrics such as peak signal to ratio, mean square error and structural similarity index. Further, the proposed approach has been compared with state of the art using metrics such as Jaccard index and dice similarity on the basis of segmentation outcomes on different pre-processing approaches.

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Section: Filtering Based Approaches [27-29]mentioning

confidence: 99%

Pre-processing of Retinal Images for Removal of Outliers

Thakur

Juneja

2020

Wireless Pers Commun

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“…As part of this study, cup disc ratio (CDR) [22], disc damage likelihood scale (DDLS) [23][24] and ISNT rule [25][26], segmented fundus images are used to calculate these features.…”

Section: Fundus Image Feature Extractionmentioning

confidence: 99%

A Deep Learning Model for Neural Network Optimization for Glaucoma Classification Using Fundus and Oct Feature Fusion

Nagamani K

2024

jes

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Irreversible vision loss is a common consequence of glaucoma, demands accurate and timely diagnosis for effective management. This research aims to enhance glaucoma classification accuracy by fusing information from two distinct imaging modalities like using deep learning explores the fusion of these modalities through an innovative neural network architecture with optimization. This approach combines Deep Stochastic Variational Autoencoder Convolution Neural Networks (DSVAECNN) and Adam optimization techniques to enable robust and accurate classification of glaucoma. A multi-path architecture is designed to accommodate both imaging as optical coherence tomography (OCT) radiomics features and fundus morphological features simultaneously. To ensure the effectiveness of the model, this study investigates the implementation of advanced optimization algorithm such as Adam, to expedite convergence and alleviate the risk of over fitting. The resulting model demonstrates improved generalization capabilities, critically for accurate diagnosis across diverse patient populations. A comprehensive OCT and fundus images dataset is used to evaluate the proposed approach from a representative cohort of glaucoma patients and healthy individuals. Quantitative various metrics including sensitivity, accuracy and specificity are employed toward appraise the accomplishment of the fusion-based classification model. Comparisons with current techniques show the dominance of the proposed move toward in accurately detecting glaucoma cases. This research provides the advancement of glaucoma diagnosis by effectively harnessing the synergy between fundus image and OCT scans and findings helpful information for clinics, ultimately facilitating early detection and personalized management of glaucoma, thus preserving vision for affected individuals.

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“…For optimal hyperparameter tuning of the DSVAE approach, the FHO algorithm is involved. The FHO technique stimulates the fire hawks (FH) foraging behavior, considering the process of capturing prey, spreading, and starting flames [23]. Firstly, a group of potential solutions (X) is defined according to the prey's position vectors and FH.…”

Section: Hyperparameter Tuningmentioning

confidence: 99%

Multi-Modal Imaging-Based Feature Fusion for Accurate Glaucoma Diagnosis with Deep Learning

Krishna¹,

Nagamani²

2023

Preprint

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Glaucoma, a leading cause of irreversible blindness worldwide, affects over 76 million people currently and is projected to increase to 111.8 million by 2040 according to the World Health Organization. Glaucoma is often asymptomatic and progresses gradually, making early detection and treatment crucial to prevent irreversible vision loss. Ophthalmologists commonly use eye analysis machines such as ophthalmoscopy and fundus photography to obtain scans for accurate diagnosis and treatment of glaucoma. However, clinical-decision support techniques in medical imaging face challenges in interpretability and reliability. To overcome these challenges and accurately detect glaucoma, this study presents a novel Fire Hawk Optimized Deep Learning Based Retinal Disease Grading and Classification (FHODLB-RDGC) utilizing multi-modal feature fusion from Optical Coherence Tomography (OCT) and Fundus images. Radiomics features for OCT images using DL Technique and cup-to-disc ratio for fundus images using NestNet model are extracted using Deep Learning (DL) techniques and merged. The proposed FHODLB-RDGC technique is of great interest in the field of ophthalmology and has demonstrated enhanced retinal disease classification results in extensive simulations on a benchmark database, particularly in accurately diagnosing glaucoma. Therefore, glaucoma, a severe and potentially blinding retinal disease, has the potential of being more accurately recognised and treated early on owing to the FHODLB-RDGC technique.

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Assessment of Disc Damage Likelihood Scale (DDLS) for Automated Glaucoma Diagnosis

Cited by 8 publications

References 7 publications

Pre-processing of Retinal Images for Removal of Outliers

Pre-processing of Retinal Images for Removal of Outliers

A Deep Learning Model for Neural Network Optimization for Glaucoma Classification Using Fundus and Oct Feature Fusion

Multi-Modal Imaging-Based Feature Fusion for Accurate Glaucoma Diagnosis with Deep Learning

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