ArticleFrontiers in bioengineering and biotechnology2021
Retinal Vessel Segmentation Algorithm Based on Residual Convolution Neural Network.
Article in Frontiers in bioengineering and biotechnology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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5 citing papers in PubMed.
- QuantumNet: An enhanced diabetic retinopathy detection model using classical deep learning-quantum transfer learning.MethodsX · 2025Article
- Recent advances in the application of artificial intelligence in age-related macular degeneration.BMJ open ophthalmology · 2024Review
- Improved fully convolutional neuron networks on small retinal vessel segmentation using local phase as attention.Frontiers in medicine · 2023Article
- A Two-To-One Deep Learning General Framework for Image Fusion.Frontiers in bioengineering and biotechnology · 2022Article
- Generative Adversarial Network Combined with SE-ResNet and Dilated Inception Block for Segmenting Retinal Vessels.Computational intelligence and neuroscience · 2022Article
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5 authors.
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Abstract
Retinal vessels are the only deep micro vessels that can be observed in human body, the accurate identification of which has great significance on the diagnosis of hypertension, diabetes and other diseases. To this end, a retinal vessel segmentation algorithm based on residual convolution neural network is proposed according to the characteristics of the retinal vessels on fundus images. Improved residual attention module and deep supervision module are utilized, in which the low-level and high-level feature graphs are joined to construct the encoder-decoder network structure, and atrous convolution is introduced to the pyramid pooling. The experiments result on the fundus image data set DRIVE and STARE show that this algorithm can obtain complete retinal vessel segmentation as well as connected vessel stems and terminals. The average accuracy on DRIVE and STARE reaches 95.90 and 96.88%, and the average specificity is 98.85 and 97.85%, which shows superior performance compared to other methods. This algorithm is verified feasible and effective for retinal vessel segmentation of fundus images and has the ability to detect more capillaries.
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