Automatic identification of diabetic retinopathy stages by using fundus images and visibility graph method

Mohammadpoory, Zeynab; Nasrolahzadeh, Mahda; Mahmoodian, Naghmeh; Haddadnia, Javad

doi:10.1016/j.measurement.2019.02.089

Cited by 20 publications

(13 citation statements)

References 32 publications

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“…Additional approaches for evaluating DR levels at the initial stage include using the visual graphic (VG) method for the classification of images and then the performance is calculated using error-correcting output codes (ECOC) classifier [ 53 ]. The study in [ 54 , 55 ], explained the use of pretrained CNN models with a transfer learning approach for the refinement of images.…”

Section: Significance Of Deep Learning Applications Using Medical Ima...mentioning

confidence: 99%

Deep Learning and Medical Image Processing Techniques for Diabetic Retinopathy: A Survey of Applications, Challenges, and Future Trends

Uppamma

Bhattacharya

2023

Journal of Healthcare Engineering

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Diabetic retinopathy (DR) is a common eye retinal disease that is widely spread all over the world. It leads to the complete loss of vision based on the level of severity. It damages both retinal blood vessels and the eye’s microscopic interior layers. To avoid such issues, early detection of DR is essential in association with routine screening methods to discover mild causes in manual initiation. But these diagnostic procedures are extremely difficult and expensive. The unique contributions of the study include the following: first, providing detailed background of the DR disease and the traditional detection techniques. Second, the various imaging techniques and deep learning applications in DR are presented. Third, the different use cases and real-life scenarios are explored relevant to DR detection wherein deep learning techniques have been implemented. The study finally highlights the potential research opportunities for researchers to explore and deliver effective performance results in diabetic retinopathy detection.

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Section: Significance Of Deep Learning Applications Using Medical Ima...mentioning

confidence: 99%

Deep Learning and Medical Image Processing Techniques for Diabetic Retinopathy: A Survey of Applications, Challenges, and Future Trends

Uppamma

Bhattacharya

2023

Journal of Healthcare Engineering

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“…In order to investigate the graph topology many features like connection, distance and spectral class are explored in the graph theory context. 12 Moreover, domain expert knowledge is required to extract the optimal visual features from the retinal images. Therefore, a novel gradient location-orientation histogram and DColor-SIFT methods are used to describe the color variations during the change in illumination.…”

Section: Related Workmentioning

confidence: 99%

“…Let 𝒫 and 𝒮 represents the zero padding and stride of convolution filter. To determine the computational cost of standard convolution layer, an multiply-adds metrics is computed as shown in Equation (12).…”

Section: Predictions In the Recurrent Convolution Neural Network Classifiermentioning

confidence: 99%

Cloud‐based onboard prediction and diagnosis of diabetic retinopathy

Sundharamurthy¹,

Kaliappan

2021

Concurrency and Computation

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Diabetic retinopathy remains the most daunting microvascular complication of diabetic disease. Major risk factors of diabetic retinopathy include long-term diabetics, improper blood glucose level, and dyslipidemia. In order to investigate the presence of diabetic retinopathy-related features and to deal with the diagnosis of various diabetic severity stages, there is a need for a cloud-based automated screening system. So, the development of novel biomarkers should take the advantage of objective measurement and evaluation indicating the biological and pathogenic processes to all the therapeutic interventions. Current state-of-the-art research studies lack accommodating large sample size, cross-validation among the diverse populations, time, and cost-effective diagnosis techniques. To overcome these challenges, a cloud-based diabetic retinopathy prediction system is proposed using the recurrent convolution neural network classifier model. The proposed prediction system can provide risk stratification, optimized resource allocation, a severe form of disease prediction, risk control, and preventive interventions during the screening and diagnosis process. It also provides prognostic information to patients stating various cost-effective predictive responses according to treatment modalities. Moreover, the proposed system clinical decision-making will outperform the existing classifiers in terms of prediction accuracy, sensitivity, specificity, precision, recall, and F1-score.

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“…There are many worthy research topics in network science including but not limiting to community detection [16], fractal dimension [17,18], link prediction [19], evolutionary game theory [20][21][22], self similarity analysis [23] and so forth. Algorithms and tools in network science can also be used for time series analysis [24,25], pattern recognition [26,27], multi-criteria decision making [28,29], uncertainty modeling [30], recommender system [31,32], just to name a few. We will see the emerging progress in both the theory and the applications of network science in the near future.…”

Section: Introductionmentioning

confidence: 99%

A modified gravity model based on network efficiency for vital nodes identification in complex networks

Li¹,

Shang²,

Deng³

2021

Preprint

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Vital nodes identification is an essential problem in network science. Various methods have been proposedto solve this problem. In particular, based on the gravity model, a series of improved gravity models are proposed to find vital nodes better in complex networks. However, they still have the room to be improved.In this paper, a novel and improved gravity model, which is named network efficiency gravity centrality model (NEG), integrates gravity model and network efficiency is proposed. Compared to other methods based on different gravity models, the proposed method considers the effect of the nodes on structure robustness of the network better. To solidate the superiority of the proposed method, experiments on varieties of real-world networks are carried out.

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Automatic identification of diabetic retinopathy stages by using fundus images and visibility graph method

Cited by 20 publications

References 32 publications

Deep Learning and Medical Image Processing Techniques for Diabetic Retinopathy: A Survey of Applications, Challenges, and Future Trends

Deep Learning and Medical Image Processing Techniques for Diabetic Retinopathy: A Survey of Applications, Challenges, and Future Trends

Cloud‐based onboard prediction and diagnosis of diabetic retinopathy

A modified gravity model based on network efficiency for vital nodes identification in complex networks

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