GC-NET for classification of glaucoma in the retinal fundus image

Juneja, Mamta; Thakur, Niharika; Thakur, Sarthak; Uniyal, Archit; Wani, Anuj; Jindal, Prashant

doi:10.1007/s00138-020-01091-4

Cited by 22 publications

(12 citation statements)

References 33 publications

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“…The performance measures such as sensitivity, specificity and accuracy for the proposed HDNN-FGSA are compared with the other state-of-art methods like LS-SVM [7], GC-NET [45], MFV-DBN [46], and HG-SVNN [32]. The comparisons of the above-stated metrics are demonstrated in Fig 5.…”

Section: Experimental Evaluations and Discussionmentioning

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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Section: Experimental Evaluations and Discussionmentioning

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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“…Compared with the traditional feature extraction method, the feature extraction method of deep learning can obtain the image features at a deeper level, which makes the image expression ability richer. The research contents of this project mainly include data enhancement, the combination of DPN92 [11] model and GCNet [12] model, model deployment and the diagnosis of CR and CT images of pneumonia on the front page [22], etc.…”

Section: Model Design and Improvementmentioning

confidence: 99%

Design of an Online Detection System for COVID-19 based on Attention Mechanism

Fan¹,

Xiang²,

Wang³

et al. 2021

FSP

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Corona Virus Disease 2019 (COVID-19) is an acute respiratory infection caused by the 2019 novel coronavirus infection that was discovered in some hospitals in Wuhan, China in December 2019. From a global perspective, COVID-19 epidemic is still in a pandemic period. Every country is taking timely preventive measures. Therefore, it is necessary to use Artificial Intelligence (AI) identification to assist radiologists in diagnosing COVID-19. This paper proposes a network model of dynamic self-attention machine for pneumonia images. The model adopts DPN92 and GCNet. It implements CR images and CT image classification of COVID-19. A COVID-19 detection system based on dynamic auto-attention machine is designed. The lung CT or CR images that meet the design requirements will be uploaded through the browser, and the background server will analyze and process the lung images, and finally output the detection category of the lung images.

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“…Afterwards, soft thresholding is applied to retinal images using shearlet transformation by [9]. Juneja et al [10] employed a window-based approach using an adaptive median kernel on grayscale pixel intensities. Hu et al [11] recently applied shearlet filters to retinal images after noise redistribution.…”

Section: Introductionmentioning

confidence: 99%

“…Pinto et al [15] utilized transfer learning (TL) on ROI of public datasets with VGG, Xception, Inception V3, and ResNet50. Afterwards, Juneja et al [10] introduced a 76-layered glaucoma classification-Net (GC-Net) CNN for the classification of filter-based denoised retinal input. However, only Conv2D layers were employed, and its variants were not tested.…”

Section: Introductionmentioning

confidence: 99%

Glaucoma classification using a polynomial-driven deep learning approach

Naidana¹,

Barpanda²

2023

Bulletin EEI

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In this paper, a deep learning-based multi-stage polynomial driven glaucoma classification-net (PDGC-Net) has been proposed for glaucoma identification through retinal images. The proposed approach begins with retinal image pu[1]rification by noise estimation and reduction. Noise has been estimated using a polynomial coefficient-based approach. Images are classified using PDGC-Net, whose polynomial indeterminate representative blocks are designed using new convolutional neural networks (CNN) architectures. The performance of PDGC[1]Net has been observed on the ACRIMA, ORIGA, and retinal image database for optic nerve evaluation (RIM-ONE) datasets. The experimentation is carried out on noisy and denoised images separately, and PDGC-Net has achieved 96% to 98% and 98% to 100% accuracy ranges, respectively. The model’s elasticity is tested with various stages of PDGC-Net. The quantitative PDGC-Net perfor[1]mance analysis is done with state-of-the-art CNN models. The proposed model’s performance has been proven and could be an effective aid to ophthalmologists for glaucoma screening (GS).

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GC-NET for classification of glaucoma in the retinal fundus image

Cited by 22 publications

References 33 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

Design of an Online Detection System for COVID-19 based on Attention Mechanism

Glaucoma classification using a polynomial-driven deep learning approach

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