ArticleScientific reports2022
A new detection model of microaneurysms based on improved FC-DenseNet.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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12 citing papers in PubMed, 26 citations in OpenAlex.
- An Improved Microaneurysms Detection for Diabetic Retinopathy Screening Using YOLO.Biomedicines · 2026Article
- Diabetic Retinopathy Detection: AI Models and Approaches.Journal of ophthalmology · 2026Review
- Real-World Evaluation of AI-Driven Diabetic Retinopathy Screening in Public Health Settings: Validation and Implementation Study.JMIR medical informatics · 2025Article
- Convolutional block attention gate-based Unet framework for microaneurysm segmentation using retinal fundus images.BMC medical imaging · 2025Article
- Monitoring of granite quarries using deep learning and UAV photogrammetry in Bengaluru, India.PloS one · 2025Article
- Use of Modified YOLOv5 Algorithm in the Differential Diagnosis of Colonic Crohn's Disease and Ulcerative Colitis on CTE Images.Gastroenterology research and practice · 2025Article
- Scheme evaluation method of coal gangue sorting robot system with time-varying multi-scenario based on deep learning.Scientific reports · 2024Article
- Predicting malignancy in breast lesions: enhancing accuracy with fine-tuned convolutional neural network models.BMC medical imaging · 2024Article
- Dual spin max pooling convolutional neural network for solar cell crack detection.Scientific reports · 2023Article
- Classification of Breast Lesions on DCE-MRI Data Using a Fine-Tuned MobileNet.Diagnostics (Basel, Switzerland) · 2023Article
- A Lightweight Hybrid Dilated Ghost Model-Based Approach for the Prognosis of Breast Cancer.Computational intelligence and neuroscience · 2022Article
- Automatic Detection of Microaneurysms in OCT Images Using Bag of Features.Computational and mathematical methods in medicine · 2022Article
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Authors and funding
6 authors at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Diabetic retinopathy (DR) is a frequent vascular complication of diabetes mellitus and remains a leading cause of vision loss worldwide. Microaneurysm (MA) is usually the first symptom of DR that leads to blood leakage in the retina. Periodic detection of MAs will facilitate early detection of DR and reduction of vision injury. In this study, we proposed a novel model for the detection of MAs in fluorescein fundus angiography (FFA) images based on the improved FC-DenseNet, MAs-FC-DenseNet. FFA images were pre-processed by the Histogram Stretching and Gaussian Filtering algorithm to improve the quality of FFA images. Then, MA regions were detected by the improved FC-DenseNet. MAs-FC-DenseNet was compared against other FC-DenseNet models (FC-DenseNet56 and FC-DenseNet67) or the end-to-end models (DeeplabV3+ and PSPNet) to evaluate the detection performance of MAs. The result suggested that MAs-FC-DenseNet had higher values of evaluation metrics than other models, including pixel accuracy (PA), mean pixel accuracy (MPA), precision (Pre), recall (Re), F1-score (F1), and mean intersection over union (MIoU). Moreover, MA detection performance for MAs-FC-DenseNet was very close to the ground truth. Taken together, MAs-FC-DenseNet is a reliable model for rapid and accurate detection of MAs, which would be used for mass screening of DR patients.
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