Diabetic Retinopathy Detection Based on Eigenvalues of the Hessian Matrix

Rubini, Stéphane; Kunthavai, A.

doi:10.1016/j.procs.2015.04.001

Cited by 22 publications

(3 citation statements)

References 13 publications

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“…For the diagnosis of DR, there are studies for each of these stages. For example, Rubini and Kunthavai [17] proposed to apply hessian-based candidate selection before the feature extraction and classification using a support vector machine (SVM) classifier. Mookiah et al [18] proposed a system that used hybrid features, including exudate/vessel area, texture, and entropy, for DR classification.…”

Section: B Traditional Practicementioning

confidence: 99%

Diagnosis of Diabetic Retinopathy Using Deep Neural Networks

et al. 2019

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Diabetic retinopathy (DR) is a common eye disease and a significant cause of blindness in diabetic patients. Regular screening with fundus photography and timely intervention is the most effective way to manage the disease. The large population of diabetic patients and their massive screening requirements have generated interest in a computer-aided and fully automatic diagnosis of DR. Deep neural networks, on the other hand, have brought many breakthroughs in various tasks in the recent years. To automate the diagnosis of DR and provide appropriate suggestions to DR patients, we have built a dataset of DR fundus images that have been labeled by the proper treatment method that is required. Using this dataset, we trained deep convolutional neural network models to grade the severities of DR fundus images. We were able to achieve an accuracy of 88.72% for a four-degree classification task in the experiments. We deployed our models on a cloud computing platform and provided pilot DR diagnostic services for several hospitals; in the clinical evaluation, the system achieved a consistency rate of 91.8% with ophthalmologists, demonstrating the effectiveness of our work. INDEX TERMS Diabetic retinopathy, automatic diagnosis, deep neural networks.

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Section: B Traditional Practicementioning

confidence: 99%

Diagnosis of Diabetic Retinopathy Using Deep Neural Networks

et al. 2019

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“…More complex. Rubini [3] proposed a DR Classification model based on sparse auto encoder of genetic algorithm. The algorithm adopts two layers of Init layer and Elite layer for feature extraction, combines genetic algorithm and softmax classifier, and uses truncated Newton constraint optimization method to train and fine-tune the layers in a supervised manner to obtain optimal weights.…”

Section: Introductionmentioning

confidence: 99%

Detection algorithm for diabetic retinopathy based on ResNet and transfer learning

Wang,

Lei

2024

International Conference on Algorithm, Imaging Processing, and Machine Vision (AIPMV 2023)

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DR (Diabetic retinopathy) a chronic progressive disease which affects eyesight and even causes blindness. It is significance to carry out the identification and severity diagnosis of DR, timely diagnosis and treatment of DR Patients, improve the people's quality, especially the elderly, and improve the efficiency of diagnosis. In this study, with the goal of efficient and accurate division of DR Levels, a DR Recognition and classification algorithm based on ResNet and transfer learning is proposed. Firstly, shallow feature extraction module of ResNet18 is used to get retinal image feature, and then the fully connected classification structure model of DR Is designed. Then the transfer learning method is combined to train the network weights to improve the generalization ability of the model, ResNet-18 is selected as the backbone network model for feature extracting. Results show that the accuracy of the training set reaches to provide useful guidance for DR Automatic diagnosis, and effectively alleviates the problem of low accuracy of DR Classification.

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“…Mixed features including area of EXs and vessels, texture and entropy, were used for DR classification by Mookiah et al,77 and three classifiers were tested. Rubini et al78 selected candidates based on hessian and used SVM classifier for classification. Bhatkar and Kharat 79 also extracted different features, such as a 64-point discrete cosine transform and other statistical features, which were processed by multi-layer perceptual neural networks.…”

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confidence: 99%

A Survey on Intelligent Screening for Diabetic Retinopathy

Dai¹,

Zhu²,

Xi³

et al. 2019

Chinese Medical Sciences Journal

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Diabetic retinopathy (DR) is one of the leading causes of vision loss and can be effectively avoided by screening, early diagnosis and treatment. In order to increase the universality and efficiency of DR screening, many efforts have been invested in developing intelligent screening, and there have been great advances. In this paper, we survey DR screening from four perspectives: 1) public color fundus image datasets of DR; 2) DR classification and related lesion-extraction approaches; 3) existing computer-aided systems for DR screening; and 4) existing issues, challenges, and research trends. Our goal is to provide insights for future research directions on DR intelligent screening.

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Diabetic Retinopathy Detection Based on Eigenvalues of the Hessian Matrix

Cited by 22 publications

References 13 publications

Diagnosis of Diabetic Retinopathy Using Deep Neural Networks

Diagnosis of Diabetic Retinopathy Using Deep Neural Networks

Detection algorithm for diabetic retinopathy based on ResNet and transfer learning

A Survey on Intelligent Screening for Diabetic Retinopathy

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