ArticleBioMed research international2021
FFU-Net: Feature Fusion U-Net for Lesion Segmentation of Diabetic Retinopathy.
Article in BioMed research international, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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13 citing papers in PubMed, 80 citations in OpenAlex.
- BigEye: a clinically interpretable deep learning framework for diabetic retinopathy detection and stage prediction.Scientific reports · 2026Article
- Advancing Visual Perception Through VCANet-Crossover Osprey Algorithm: Integrating Visual Technologies.Journal of imaging informatics in medicine · 2026Article
- Diabetic retinopathy screening using machine learning: a systematic review.BMC biomedical engineering · 2025Article
- Convolutional block attention gate-based Unet framework for microaneurysm segmentation using retinal fundus images.BMC medical imaging · 2025Article
- Detection and diagnosis of diabetic eye diseases using two phase transfer learning approach.PeerJ. Computer science · 2024Article
- SSMD-UNet: semi-supervised multi-task decoders network for diabetic retinopathy segmentation.Scientific reports · 2023Article
- Application of Deep Learning Methods in a Moroccan Ophthalmic Center: Analysis and Discussion.Diagnostics (Basel, Switzerland) · 2023Article
- Level-set based adaptive-active contour segmentation technique with long short-term memory for diabetic retinopathy classification.Frontiers in bioengineering and biotechnology · 2023Article
- Review of Semantic Segmentation of Medical Images Using Modified Architectures of UNET.Diagnostics (Basel, Switzerland) · 2022Review
- Three-Dimensional Semantic Segmentation of Diabetic Retinopathy Lesions and Grading Using Transfer Learning.Journal of personalized medicine · 2022Article
- Local Structure Awareness-Based Retinal Microaneurysm Detection with Multi-Feature Combination.Biomedicines · 2022Article
- Combining transfer learning with retinal lesion features for accurate detection of diabetic retinopathy.Frontiers in medicine · 2022Article
- Automated Detection and Diagnosis of Diabetic Retinopathy: A Comprehensive Survey.Journal of imaging · 2021Review
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Authors and funding
6 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Diabetic retinopathy is one of the main causes of blindness in human eyes, and lesion segmentation is an important basic work for the diagnosis of diabetic retinopathy. Due to the small lesion areas scattered in fundus images, it is laborious to segment the lesion of diabetic retinopathy effectively with the existing U-Net model. In this paper, we proposed a new lesion segmentation model named FFU-Net (Feature Fusion U-Net) that enhances U-Net from the following points. Firstly, the pooling layer in the network is replaced with a convolutional layer to reduce spatial loss of the fundus image. Then, we integrate multiscale feature fusion (MSFF) block into the encoders which helps the network to learn multiscale features efficiently and enrich the information carried with skip connection and lower-resolution decoder by fusing contextual channel attention (CCA) models. Finally, in order to solve the problems of data imbalance and misclassification, we present a Balanced Focal Loss function. In the experiments on benchmark dataset IDRID, we make an ablation study to verify the effectiveness of each component and compare FFU-Net against several state-of-the-art models. In comparison with baseline U-Net, FFU-Net improves the segmentation performance by 11.97%, 10.68%, and 5.79% on metrics SEN, IOU, and DICE, respectively. The quantitative and qualitative results demonstrate the superiority of our FFU-Net in the task of lesion segmentation of diabetic retinopathy.
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