Shape and texture based identification of glaucoma from retinal fundus images

Sonti, Kamesh; Dhuli, Ravindra

doi:10.1016/j.bspc.2021.103473

Cited by 10 publications

(5 citation statements)

References 31 publications

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“…1 In the era of ophthalmology, glaucoma is a leading disorder that can arise due to increase in intra-ocular pressure (IOP) above its normal range of 15-21 mmHg. 2 This results in the damage of optic nerve, which carries data to the human brain, hence altering the shape and structure of the optic disc. As per the latest reports of the world health organization (WHO), more than 80 million people are suffering currently and very soon it may reach to 112 million.…”

Section: Glaucoma Overviewmentioning

confidence: 99%

“…In recent years, diagnosis of various medical diseases is rapidly increasing using imaging techniques because of its coverage of internal and external organs 1 . In the era of ophthalmology, glaucoma is a leading disorder that can arise due to increase in intra‐ocular pressure (IOP) above its normal range of 15–21 mmHg 2 . This results in the damage of optic nerve, which carries data to the human brain, hence altering the shape and structure of the optic disc.…”

Section: Glaucoma Overviewmentioning

confidence: 99%

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Diagnosis of glaucoma from retinal fundus images using disc localization and sequential DNN model

Sonti,

Dhuli

2024

Int J Imaging Syst Tech

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Deep learning is an emerging trend with enormous applications over the past few years. Ophthalmology is one such area in medical applications where early disease detection is required to avoid loss of vision. Glaucoma is a rapidly growing disorder related to human eye, which arises due to the increase in pressure inside the eye. The medical diagnosis methods available for glaucoma have some limitations; hence, computer‐aided design (CAD) approach is preferred using images. In the context of image processing, convolution neural networks (CNNs) are preferred for classification because of their ability to grasp highly discriminate features from raw pixel intensities. In our approach, diagnosis of glaucoma is implemented by extracting the region of interest (ROI) by splitting the coefficients into recurrence decays and will improve the possibility of identifying even poorly differentiated exudates and upgrading the normal recurrence ranges. Later, a sequential deep neural network (DNN) model with a rectified linear unit (ReLU) and sigmoid function is designed to train the data with effective features matching from training and testing samples. The proposed model is implemented on two publicly available datasets (Drishti‐GS1 and ACRIMA) using 10‐fold cross validation (CV), 60:40 and 70:30 split ratio approaches, and performance is assessed using the metrics and plotted the region of convergence curves. The model is also tested on two more datasets (ORIGA and Refuge) to validate the robustness of the proposed model. The obtained simulation results and the evaluated performance metrics prove that our proposed model diagnose glaucoma from retinal fundus images effectively compared with other existing models.

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Section: Glaucoma Overviewmentioning

confidence: 99%

Section: Glaucoma Overviewmentioning

confidence: 99%

Diagnosis of glaucoma from retinal fundus images using disc localization and sequential DNN model

Sonti,

Dhuli

2024

Int J Imaging Syst Tech

Self Cite

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“…All these are computed using the vertical disc and cup heights. Automatic methods thus generally rely on analysis of the optic disc region for glaucoma diagnosis [16][17][18][19]. Three types of glaucoma detection algorithms are commonly presented in the literature: structural, generic, and hybrid.…”

Section: Iridocorneal Endothelial Syndromementioning

confidence: 99%

Creating an Early Diagnostic Method for Glaucoma Using Convolutional Neural Networks

Alqarni,

Al Harbi,

Subhan

2024

Preprint

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According to the World Health Organization, glaucoma is a leading cause of blindness, accounting for over 12% of global blindness as it affects one in every 100 people. In fact, 79.6 million people worldwide live with blindness caused by glaucoma. This is because the current method for diagnosing glaucoma is by examining retinal fundus images. However, it is considerably difficult to distinguish the lesions' features solely through manual observations by ophthalmologists, especially in the early phases. This study proposes a new diagnosis method using convolutional neural networks. The attention mechanism is utilized to learn pixel-wise features for accurate prediction. Several attention strategies have been developed to guide the networks in learning the important features and factors that affect localization accuracy. The algorithms were trained for glaucoma detection using Python 2.7, TensorFlow, Py Torch, and Keras. The methods were evaluated on Drishti-GS and RIM-ONE datasets with 361 training and 225 test sets, consisting of 344 healthy and 242 glaucomatous images. The proposed algorithms can achieve impressive results that show an increase in overall diagnostic efficiency, as the algorithm displays a 30-second detection time with 98.9% accuracy compared to the 72.3% accuracy of traditional testing methods. Finally, this algorithm has been implemented as a webpage, allowing patients to test for glaucoma. This webpage offers various services such as: connecting the patient to the nearest care setup; offering scientific articles regarding glaucoma; and a video game that supports eye-treatment yogic exercises to strengthen vision and focus. This early diagnostic method has the near future potential to decrease the percentage of irreversible vision loss due to glaucoma by 42.79% (the percentage was calculated using the mean absolute error function), which could prevent glaucoma from remaining the leading cause of blindness worldwide.

show abstract

Section: Iridocorneal Endothelial Syndromementioning

confidence: 99%

Creating an Early Diagnostic Method for Glaucoma Using Convolutional Neural Networks

Alqarni,

Al-Harbi,

Subhan

2024

Preprint

View full text Add to dashboard Cite

According to the World Health Organization, glaucoma is a leading cause of blindness, accounting for over 12% of global blindness as it affects one in every 100 people. In fact, 79.6 million people worldwide live with blindness caused by glaucoma. This is because the current method for diagnosing glaucoma is by examining retinal fundus images. However, it is considerably difficult to distinguish the lesions' features solely through manual observations by ophthalmologists, especially in the early phases. This study introduces a novel glaucoma detection method using attention-enhanced convolutional neural networks, achieving 98.9% accuracy and a swift 30-second detection time, vastly surpassing traditional diagnostic methods. The attention mechanism is utilized to learn pixel-wise features for accurate prediction. Several attention strategies have been developed to guide the networks in learning the important features and factors that affect localization accuracy. The algorithms were trained for glaucoma detection using Python 2.7, TensorFlow, Py Torch, and Keras Machine Learning-Based Applications. The methods were evaluated on Drishti-GS and RIM-ONE datasets with 361 training and 225 test sets, consisting of 344 healthy and 242 glaucomatous images. The proposed algorithms can achieve impressive results that show an increase in overall diagnostic efficiency, as the algorithm displays a 30-second detection time with 98.9% accuracy compared to the 72.3% accuracy of traditional testing methods. Finally, this algorithm has been implemented as a webpage, allowing patients to test for glaucoma. This webpage offers various services such as: connecting the patient to the nearest care setup; offering scientific articles regarding glaucoma; and a video game that supports eye-treatment yogic exercises to strengthen vision and focus. This early diagnostic method has the near future potential to decrease the percentage of irreversible vision loss due to glaucoma by 42.79% (the percentage was calculated using the mean absolute error function), which could prevent glaucoma from remaining the leading cause of blindness worldwide. Our glaucoma diagnostic webpage can be found at: Glaucoma Detector (glaucomadiagnosis.com)

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Shape and texture based identification of glaucoma from retinal fundus images

Cited by 10 publications

References 31 publications

Diagnosis of glaucoma from retinal fundus images using disc localization and sequential DNN model

Diagnosis of glaucoma from retinal fundus images using disc localization and sequential DNN model

Creating an Early Diagnostic Method for Glaucoma Using Convolutional Neural Networks

Creating an Early Diagnostic Method for Glaucoma Using Convolutional Neural Networks

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