Retinal vessel segmentation to diagnose diabetic retinopathy using fundus images: A survey

Radha, K.; Karuna, Yepuganti

doi:10.1002/ima.22945

Cited by 5 publications

(1 citation statement)

References 154 publications

(235 reference statements)

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“…The use of DNNs-based methods shows superior performance in handling complexity in fundus images as it learns features directly from the training data [28] [29]. There are many methods available for diagnosing diabetic retinopathy through retinal vessel segmentation [30].…”

Section: ⅱ Related Workmentioning

confidence: 99%

Modified Depthwise Parallel Attention UNet for Retinal Vessel Segmentation

Radha,

Karuna

2023

IEEE Access

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Retinal fundus images contain highly informative geometrical features for detecting diabetic retinopathy (DR), including vessels, especially thin and low-contrast vessels, which are predominant features for accurately diagnosing diabetic retinopathy. Automatic segmentation methods have been developed based on deep convolutional neural networks to replace manual labeling. These methods have shown acceptable performance in fundus vessel segmentation. The UNet model is a well-known architecture of deep neural networks often used for vessel segmentation tasks and has achieved significant performance. However, segmentation tasks remain challenging due to multiple convolutions, down-sampling operations, and inadequate feature fusion in the encoder-decoder architecture. Also, traditional convolution increases the number of multiplications while performing convolution operations. These challenges lead to the loss of information related to thin and low-contrast vessels, eventually affecting the segmentation performance. To tackle this issue, we propose incorporating depthwise parallel attention in the existing UNet framework (DPA-UNet) to achieve accurate vessel segmentation. This approach entails the integration of a depthwise convolution block in the downsampling path and a parallel attention mechanism in the upsampling path of UNet. The primary benefit of depthwise convolution and global information embedding (GIE) is the ability to capture intricate information characteristics across channels. This helps to minimize the information degradation caused by conventional convolution and downsampling techniques. A parallel attention network is proposed in the upsampling path of the existing UNet to optimize the channel and spatial information acquired from the encoder-decoder. Extensive experiments are conducted on three publicly available datasets, namely DRIVE, STARE, and CHASE_DB1, to validate the performance of the proposed model. The findings indicate that the UNET model with depthwise parallel attention achieved a competitive performance with fewer network parameters in segmenting retinal vessels.

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Section: ⅱ Related Workmentioning

confidence: 99%

Modified Depthwise Parallel Attention UNet for Retinal Vessel Segmentation

Radha,

Karuna

2023

IEEE Access

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A Novel Classification Approach for Retinal Disease Using Improved Gannet Optimization‐Based Capsule DenseNet

Venkatesan,

Kempanna,

Nagaraja

et al. 2024

Int J Imaging Syst Tech

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An unusual condition of the eye called diabetic retinopathy affects the human retina and is brought on by the blood's constant rise in insulin levels. Loss of vision is the result. Diabetic retinopathy can be improved by receiving an early diagnosis to prevent further damage. A cost‐effective method of accumulating medical treatments is through appropriate DR screening. In this work, deep learning framework is introduced for the accurate classification of retinal diseases. The proposed method processes retinal fundus images obtained from databases, addressing noise and artifacts through an improved median filter (ImMF). It leverages the UNet++ model for precise segmentation of the disease‐affected regions. UNet++ enhances feature extraction through cross‐stage connections, improving segmentation results. The segmented images are then fed as input to the improved gannet optimization‐based capsule DenseNet (IG‐CDNet) for retinal disease classification. The hybrid capsule DenseNet (CDNet) classifies disease and is optimized using the improved gannet optimization algorithm to boost classification accuracy. Finally, the accuracy and dice score values achieved are 0.9917 and 0.9652 on the APTOS‐2019 dataset.

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