Classification of retinal images with deep learning for early detection of diabetic retinopathy disease

Yalçin, Nursel; Alver, Seyfullah; Uluhatun, Necla

doi:10.1109/siu.2018.8404369

Cited by 23 publications

(2 citation statements)

References 7 publications

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“…Recovery productivity is determined by accuracy and review. The outcomes show that the Kirsch format-based edge recognition strategy distinguishes the majority of the veins contrasted with the other technique [ 6 , 7 ]. A serious level of accuracy and review is noticed utilizing the Kirsch format-based CBIR framework.…”

Section: Literature Surveymentioning

confidence: 99%

[Retracted] Recognition of Diabetic Retinopathy with Ground Truth Segmentation Using Fundus Images and Neural Network Algorithm

Kshirsagar

Manoharan²,

Meshram³

et al. 2022

Computational Intelligence and Neuroscience

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Diabetes problems can lead to a condition called diabetic retinopathy (DR), which permanently damages the blood vessels in the retina. If not treated, DR is a significant cause of blindness. The only DR treatments currently accessible are those that block or delay vision loss, which emphasizes the value of routine scanning with high-efficiency computer-based technologies to identify patients early. The major goal of this study is to employ a deep learning neural network to identify diabetic retinopathy in the retina’s blood vessels. The NN classifier is put to the test using the input fundus image and DR database. It effectively contrasts retinal images and distinguishes between classes when there is a legitimate edge. For the resolution of the problems in the photographs, it is particularly useful. Here, it will be tested to see if the classification of diabetic retinopathy is normal or abnormal. Modifying the existing study’s conclusion strategy, existing diabetic retinopathy techniques have sensitivity, specificity, and accuracy levels that are much lower than what is required for this research.

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Section: Literature Surveymentioning

confidence: 99%

[Retracted] Recognition of Diabetic Retinopathy with Ground Truth Segmentation Using Fundus Images and Neural Network Algorithm

Kshirsagar

Manoharan²,

Meshram³

et al. 2022

Computational Intelligence and Neuroscience

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“…Recently, automated systems for detecting diabetic retinopathy stages have widely explored and gained a lot of acceptances [10,11,12,13,14,15,16]. Azzopardi et.…”

Section: Introductionmentioning

confidence: 99%

Diabetic Retinopathy Detection Based on Deep Learning

Liang¹,

Li²,

Deng³

2019

FSP

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Recent years, deep learning in the image identification has made great progress, showing good application prospects in medical image reading. Diabetic Retinopathy (DR) is an eye disease due to diabetes, which is the most ordinary cause of blindness. Traditional diabetic retinopathy detection is a manual and time-consuming and labor-intensive process, which requires a highly experienced clinician to examine and evaluate the digital color fundus photos of the retina. Therefore, it is crucial to use the deep learning technique to achieve automatic detection of diabetic retinopathy. In this paper, we proposed a diabetic retinopathy detection method based on deep learning and proposed a network structure named multi-self-attention. At first, the image features were extracted through the InceptionV3 model, and then the feature maps was directly generated. Secondly, the feature maps, which can reflect condition of retina, will be input into multi-self-attention network structure, to calculate multi-self-attention feature. Finally, by convolutional layer and fully connected layer, the stage results about diabetic retinopathy will be obtained. With the experiments in TensorFlow framework , the effectiveness of multi-self-attention network structure for feature extraction and classification is proved.

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Diabetic Retinopathy Detection and Classification Using Deep Reinforcement Learning

Manjunatha,

Sathish

2024

Deep Reinforcement Learning and Its Industrial Use Cases

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Classification of retinal images with deep learning for early detection of diabetic retinopathy disease

Cited by 23 publications

References 7 publications

[Retracted] Recognition of Diabetic Retinopathy with Ground Truth Segmentation Using Fundus Images and Neural Network Algorithm

[Retracted] Recognition of Diabetic Retinopathy with Ground Truth Segmentation Using Fundus Images and Neural Network Algorithm

Diabetic Retinopathy Detection Based on Deep Learning

Diabetic Retinopathy Detection and Classification Using Deep Reinforcement Learning

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