Earlier glaucoma detection using blood vessel segmentation and classification

E, Deepika; Maheswari, S.

doi:10.1109/icisc.2018.8399120

Cited by 17 publications

(6 citation statements)

References 10 publications

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“…The noise from the image is removed and the image is transformed into black and white. From this, a new threshold value is evaluated to separate the blood vessels [33].…”

Section: Segmentation Phase By Blood Vessels By Thresholding Methodsmentioning

confidence: 99%

FGS-HDNN: Fractional Gravitational Search based Hybrid Deep Neural Network for Glaucoma Detection using Fundus Images

Madhumalini

Devi

2021

Preprint

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Glaucoma is a retinal disease that damages the eye's optic nerve, frequently causing an irreversible loss of vision. However, the accurate diagnosis of this disease is difficult but early-stage diagnosis may cure this retinal disease. The objective of this research is to diagnose glaucoma disease in the top of the eye's optical nerve. The proposed approach detects glaucoma via four major steps namely Data enhancement phase, segmentation phase, feature extraction phase, and classification phase by the fractional gravitational search-based hybrid deep neural network (FGSA-HDNN) classifier. The proposed classifier is used for the exact classification of glaucoma infected images and normal images. Here, the proposed approach utilizes the statistical, textural, and vessel features from the segmented output. Also, the proposed FGSO algorithm is used for testing the deep neural network. From the experimental results, it is observed that the proposed glaucoma detection has obtained a sensitivity of 99.64%, a specificity of 97.84%, and an accuracy of 98.75% that outperforms other state-of-art methods.

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“…The noise from the image is removed and the image is transformed into black and white. From this, a new threshold value is evaluated to separate the blood vessels [33].…”

Section: Segmentation Phase By Blood Vessels By Thresholding Methodsmentioning

confidence: 99%

FGS-HDNN: Fractional Gravitational Search based Hybrid Deep Neural Network for Glaucoma Detection using Fundus Images

Madhumalini

Devi

2021

Preprint

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“…Furthermore, Xu and coworkers implemented external testing; this achieved the metrics of 98.4% sensitivity and 94.1% specificity; indicating the generalizability of their adopted approach. Deepika and Maheswari did not specify the kernel used, this framework yielded an accuracy of 91.67%, sensitivity of 90% and specificity of 93.3% ( 29 ). Likewise, Yunitasari and coworkers did not specify the kernel used; their proposed framework achieved an accuracy of 95%, sensitivity of 91.4% and specificity of 95.6% ( 97 ).…”

Section: Machine Learning/statistical Modeling-based Ai Classifiers A...mentioning

confidence: 99%

Automatic detection of glaucoma via fundus imaging and artificial intelligence: A review

Coan¹,

Williams²,

Venkatesh³

et al. 2023

Survey of Ophthalmology

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“…In the scope of detecting glaucoma using blood vessel segmentation, the authors of [35] developed a method for classifying the severity of the pathology. As a first stage, they extracted features from the vessel images, which were used as inputs to a hybrid model composed of an adaptive neural-fuzzy inference system (ANFIS) and an SVM.…”

Section: Blood Vesselmentioning

confidence: 99%

Deep Transfer Learning Strategy to Diagnose Eye-Related Conditions and Diseases: An Approach Based on Low-Quality Fundus Images

2023

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Data from the World Health Organization indicate that billion cases of visual impairment could be avoided, mainly with regular examinations. However, the absence of specialists in basic health units has resulted in a lack of accurate diagnosis of systemic or asymptomatic eye diseases, increasing the cases of blindness. In this context, the present paper proposes an ensemble of convolutional neural networks, which were submitted to a transfer learning process by using 38,727 high-quality fundus images. Next, the ensemble was tested with 13,000 low-quality fundus images acquired by low-cost equipment. Thus, the proposed approach contributes to advance the state-of-the-art with a novel deep transfer learning strategy, which is more suitable and feasible to be applied by emerging and under-developing countries. From lowquality images, the proposed approach was able to reach accuracies of 87.4%, 90.8%, 87.5%, 79.1% to classify cataract, diabetic retinopathy, excavation and blood vessels, respectively.INDEX TERMS Convolutional neural network; deep learning; eye-related conditions; fundus images; transfer learning.

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Earlier glaucoma detection using blood vessel segmentation and classification

Cited by 17 publications

References 10 publications

FGS-HDNN: Fractional Gravitational Search based Hybrid Deep Neural Network for Glaucoma Detection using Fundus Images

FGS-HDNN: Fractional Gravitational Search based Hybrid Deep Neural Network for Glaucoma Detection using Fundus Images

Automatic detection of glaucoma via fundus imaging and artificial intelligence: A review

Deep Transfer Learning Strategy to Diagnose Eye-Related Conditions and Diseases: An Approach Based on Low-Quality Fundus Images

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