GANet: Group attention network for diabetic retinopathy image segmentation

Ye, Lei; Zhu, Weifang; Feng, Shuanglang; Chen, Xinjian

doi:10.1117/12.2548310

Cited by 5 publications

(5 citation statements)

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“…The regions are then grown from these seed points to other points in the vicinity of pixels depending on a region membership criterion (pixel intensity). To determinate whether the new point is good enough to join the selected seed location (point) or not, the mean and standard deviation of the growth region need to be computed, as depicted in Equations ( 16) and (17).…”

Section: Resultsmentioning

confidence: 99%

“…In addition, many of these algorithms are employed to manually extract the attributes related to the DR using some hand-crafted features, aimed at describing the prediction of anatomical structures in the fundus, such as optical discs, blood vessels, or macula. Though these hand-crafted representations might be run on the Foundation Individual Dataset, they are once again trying to accurately detect DR through fundus images that are tailor-made for different demographic purposes [ 15 , 17 ]. The general-purpose features, comprising GLCM (Gray Level Cooccurrence Matrix) [ 18 ], GLRM (Gray Level Run Length Matrix) [ 19 ], and Histogram of Oriented Gradients (HOG) Histogram of Oriented Gradients, have been examined using nonspecific methods applicable to specifying DR properties; however, they have shown weaker and disproportionate properties, which cannot describe the nuances of retinopathy.…”

Section: Introductionmentioning

confidence: 99%

“…This process is an arduous task for doctors and needs a lot of time. After training, systems can acquire much more rapid classifications, and this will enable them to help clinicians in a quick classification [ 17 , 20 , 21 ].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

An Extended Approach to Predict Retinopathy in Diabetic Patients Using the Genetic Algorithm and Fuzzy C-Means

Ghoushchi

Ranjbarzadeh

Dadkhah

et al. 2021

BioMed Research International

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The present study is developed a new approach using a computer diagnostic method to diagnosing diabetic diseases with the use of fluorescein images. In doing so, this study presented the growth region algorithm for the aim of diagnosing diabetes, considering the angiography images of the patients’ eyes. In addition, this study integrated two methods, including fuzzy C-means (FCM) and genetic algorithm (GA) to predict the retinopathy in diabetic patients from angiography images. The developed algorithm was applied to a total of 224 images of patients’ retinopathy eyes. As clearly confirmed by the obtained results, the GA-FCM method outperformed the hand method regarding the selection of initial points. The proposed method showed 0.78 sensitivity. The comparison of the fuzzy fitness function in GA with other techniques revealed that the approach introduced in this study is more applicable to the Jaccard index since it could offer the lowest Jaccard distance and, at the same time, the highest Jaccard values. The results of the analysis demonstrated that the proposed method was efficient and effective to predict the retinopathy in diabetic patients from angiography images.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

An Extended Approach to Predict Retinopathy in Diabetic Patients Using the Genetic Algorithm and Fuzzy C-Means

Ghoushchi

Ranjbarzadeh

Dadkhah

et al. 2021

BioMed Research International

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show abstract

“…Currently, UNet-based deep learning segmentation methods are being developed in the field of biomedical image segmentation. Ye et al [11] developed a U-shape-based algorithm that uses an attention mechanism, a method known as the group attention network, to segment the fundus images with diabetic retinopathy. This approach uses channel group and spatial group attention modules.…”

Section: Literature Reviewmentioning

confidence: 99%

Channel and Spatial Attention Aware UNet Architecture for Segmentation of Blood Vessels, Exudates and Microaneurysms in Diabetic Retinopathy

2024

IJIES

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Diabetic retinopathy stands out as one of the highly prevalent causes of vision loss in working people worldwide. In computer vision, deep learning based strategies are seen as a viable solution for efficient diabetic retinopathy detection. We present a UNet-based deep learning architecture for diabetic retinopathy segmentation of blood vessels, exudates, and microaneurysms. Traditional methods often consider the features only from the last convolution unit and discard the remaining features, resulting in low-quality feature maps. However, boundary information plays important role in medical image segmentation. To overcome this, we introduce a skip connection mechanism to concatenate all attributes from each layer. Additionally, we utilize an upsampling layer to aggregate the features at the final sigmoid layer. Finally, we apply channel and spatial attention mechanisms to generate the semantic feature map. Therefore, the proposed approach overcomes the issues of existing methods by incorporating dense skip connection along with channel and spatial attention mechanism which helps to retain the substantial information of image. We tested proposed approach on several publicly available datasets such as IDRiD, DIARETDB1, STARE, ChaseDB1, DRIVE, and HRF datasets. The comparative analysis shows that the proposed approach achieves superior performance, with an average accuracy of 98.10%, average sensitivity of 97.60%, and average specificity of 98.2% for segmentation.

show abstract

“…Currently, UNet-based deep learning segmentation methods are being developed in the field of biomedical image segmentation. Ye et al [11] developed a U-shapebased algorithm that uses an attention mechanism, a method known as the Group Attention network, to segment the fundus images with diabetic retinopathy. This approach uses Channel group and spatial group attention modules.…”

Section: Literature Reviewmentioning

confidence: 99%

Channel and Spatial Attention Aware UNet Architecture for Segmentation of Blood Vessels, Exudates and Microaneurysms in Diabetic Retinopathy

2023

Preprint

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Diabetic retinopathy stands out as one of the highly prevalent causes of vision loss in working people worldwide. In computer vision, deep learning based strategies are seen as a viable solution for efficient diabetic retinopathy detection. We present a UNet-based deep learning architecture for diabetic retinopathy segmentation of blood vessels, exudates, and microaneurysms. Traditional methods often consider the features only from the last convolution unit and discard the remaining features, resulting in low-quality feature maps. To overcome this, we introduce a skip connection mechanism to concatenate all attributes from each layer. Additionally, we utilize an upsampling layer to aggregate the features at the final sigmoid layer. Finally, we apply channel and spatial attention mechanisms to generate the semantic feature map. We tested proposed approach on several publicly available datasets such as IDRiD, DIARETDB1, STARE, ChaseDB1, DRIVE, and HRF datasets. The comparative analysis shows that the proposed approach achieves superior performance, with an average accuracy of 98.10%, average sensitivity of 97.60%, and average specificity of 98.2% for segmentation.

show abstract

GANet: Group attention network for diabetic retinopathy image segmentation

Cited by 5 publications

References 0 publications

An Extended Approach to Predict Retinopathy in Diabetic Patients Using the Genetic Algorithm and Fuzzy C-Means

An Extended Approach to Predict Retinopathy in Diabetic Patients Using the Genetic Algorithm and Fuzzy C-Means

Channel and Spatial Attention Aware UNet Architecture for Segmentation of Blood Vessels, Exudates and Microaneurysms in Diabetic Retinopathy

Channel and Spatial Attention Aware UNet Architecture for Segmentation of Blood Vessels, Exudates and Microaneurysms in Diabetic Retinopathy

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