RETRACTED ARTICLE: Early diagnosis of glaucoma using multi-feature analysis and DBN based classification

Ajesh, F.; Ravi, Renjith V.; Rajakumar, G.

doi:10.1007/s12652-020-01771-z

Cited by 19 publications

(13 citation statements)

References 26 publications

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“…77 In traditional glaucoma detection methods, wavelet features were previously shown to outperform spatial features. 25 In DL glaucoma detection approaches, there was a single attempt by Ajesh et al 87 to employ the wavelet histograms of retinal images along with optical coherence tomography images, retinal fiber layer analysis, and retinal images as inputs to a deep belief network. Wavelet histograms were thus used alongside several other modalities, by that not fully exploring the potential of wavelet images for training deep networks.…”

Section: Discussionmentioning

confidence: 99%

TWEEC: Computer‐aided glaucoma diagnosis from retinal images using deep learning techniques

Abdel-Hamid

2021

Int J Imaging Syst Tech

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A novel two-branched deep convolutional (TWEEC) network is developed for computer-aided glaucoma diagnosis. The TWEEC network is designed to simultaneously extract anatomical information related to the optic disc and surrounding blood vessels which are the retinal structures most affected by glaucoma progression. The spatial retinal images and wavelet approximation subbands are compared as inputs to the proposed network. TWEEC's performance is compared to three implemented convolutional networks, one of which employs transfer learning. Experiments showed that the introduced TWEEC network achieved accuracies of 98.78% and 96.34% for the spatial and wavelet inputs, respectively, by that outperforming the other three deep networks by 8-15%. This work paves the way for the development of efficient deep learning based computer-aided glaucoma diagnosis tools. Moreover, the present study illustrates that considering specific wavelet subbands for the training of convolutional networks can result in reliable performance with the advantage of reduced overall network training time.

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Section: Discussionmentioning

confidence: 99%

TWEEC: Computer‐aided glaucoma diagnosis from retinal images using deep learning techniques

Abdel-Hamid

2021

Int J Imaging Syst Tech

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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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“…But the error rate was not reduced during the classification. Multi-Feature Vector and Deep Belief Network (MFV-DBN) was developed in [20] to determine glaucoma at an earlier stage. Though the method increases the accuracy, the time complexity was not effectively decreased.…”

Section: Related Workmentioning

confidence: 99%

Deep Perona–malik Diffusive Mean Shift Image Classification for Early Glaucoma and Stargardt Disease Detection

Arunachalam¹,

Devaraj

Bhavani³

2023

MJCS

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Glaucoma and Stargardt’s, an inherited disease predominantly affect the retinal portion of the eye. The diagnosis of Glaucoma in a fundus image is an arduous, time consuming process. There were many research works carried out to detect early stages of Glaucoma and Stargardt’s disease. However, the accuracy, diagnostic time and performance were not improved. To resolve the above said problems, a computational method called Deep Neural Perona–Malik Diffusive Mean Shift Mode Seeking Segmented Image Classification (DNP-MDMSMSIC) is introduced for the early detection of Glaucoma and Stargardt’s disease with retinal fundus images. The DNP-MDMSMSIC method comprises diverse types of layers that support to identify early detection of disease with improved accuracy and less time. Process as explained; initially, numerous qualified retinal images are given as input to the input layer. These input images are transmitted further to the hidden layer 1 to perform image pre-processing. In DNP-MDMSMSIC, Space-Variant Perona–Malik Diffusive Image Preprocessing is carried out to decrease the noise from input image without removing contents like edges, lines, etc., for image interpretation with a higher peak signal-to-noise ratio. This preprocessed image is further processed in the hidden layer 2 where the feature extraction process is performed to extract features like color, texture, and intensity with a higher degree of accuracy. Based on the extracted features, an input feature image gets segmented in hidden layer 3. Mean Shift Mode Seeking Segmentation algorithm is employed to segment the pixels in image space with corresponding feature space points. Then the segmented images are given to the output layer to perform retinal fundus image classification using Bregman Divergence Function. During the image classification, the distance between two segmented regions (i.e., testing image region of particular class and training image region) with convex is measured. In this way, the retinal fundus images get classified with higher accuracy. Experimental evaluation is performed by considering the metrics such as peak signal-to-noise, disease detection accuracy, disease detection time, and error rate corresponding to the number of retina fundus images and image size. DNP-MDMSMSIC method is designed to detect Glaucoma and Stargardt’s disease at an earlier stage with higher accuracy by 8% and less time by 20% with aid of ACRIMA database.

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RETRACTED ARTICLE: Early diagnosis of glaucoma using multi-feature analysis and DBN based classification

Cited by 19 publications

References 26 publications

TWEEC: Computer‐aided glaucoma diagnosis from retinal images using deep learning techniques

TWEEC: Computer‐aided glaucoma diagnosis from retinal images using deep learning techniques

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

Deep Perona–malik Diffusive Mean Shift Image Classification for Early Glaucoma and Stargardt Disease Detection

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