ArticleSensors (Basel, Switzerland)2022
A New Approach for Detecting Fundus Lesions Using Image Processing and Deep Neural Network Architecture Based on YOLO Model.
Article in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers, 1 of them a synthesis that pooled it.
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Who cites it
24 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A review of optimization strategies for deep and machine learning in diabetic macular edema.Frontiers in artificial intelligence · 2026Pooled it
- Detection of Myopia from Colour Fundus Photographs Using YOLO-Based Computer-Vision Models: A Comparison with Expert Ophthalmologists.Journal of clinical medicine · 2026Article
- A YOLOv8 deep learning model for detecting labral injury via hip magnetic resonance imaging.Quantitative imaging in medicine and surgery · 2026Article
- A real-time industrial safety automation using YOLO architectures leveraging diverse chromatic domains.Scientific reports · 2026Article
- An Improved Microaneurysms Detection for Diabetic Retinopathy Screening Using YOLO.Biomedicines · 2026Article
- YOLOBT: a novel ERP bad trial detection network dynamically adjusting based on global signal quality.Frontiers in human neuroscience · 2026Article
- Diabetic Retinopathy Detection: AI Models and Approaches.Journal of ophthalmology · 2026Review
- A digital twin-driven deep learning framework for online quality inspection in tobacco transplanting.Frontiers in plant science · 2026Article
- DRCNN-Lesion Proxy: a hybrid CNN with lesion-inspired feature simulation for diabetic retinopathy severity classification.Scientific reports · 2025Article
- Advanced gesture recognition in Indian sign language using a synergistic combination of YOLOv10 with Swin Transformer model.Scientific reports · 2025Article
- An Automated Diagnosis of Myopia from an Optic Disc Image Using YOLOv11: A Feasible Approach for Non-Expert ECPs in Computer Vision.Life (Basel, Switzerland) · 2025Article
- Optic Cup and Disc Segmentation of Fundus Images Using Artificial Intelligence Externally Validated With Optical Coherence Tomography Measurements.Translational vision science & technology · 2025Article
- Hyperparameter Optimization for Tomato Leaf Disease Recognition Based on YOLOv11m.Plants (Basel, Switzerland) · 2025Article
- A multi-module enhanced YOLOv8 framework for accurate AO classification of distal radius fractures: SCFAST-YOLO.Frontiers in medicine · 2025Article
- LT-YOLO: long-term temporal enhanced YOLO for stenosis detection on invasive coronary angiography.Frontiers in molecular biosciences · 2025Article
- Diabetic Retinopathy Features Segmentation without Coding Experience with Computer Vision Models YOLOv8 and YOLOv9.Vision (Basel, Switzerland) · 2024Article
- A deep learning approach to hard exudates detection and disorganization of retinal inner layers identification on OCT images.Scientific reports · 2024Article
- Enhanced object detection in pediatric bronchoscopy images using YOLO-based algorithms with CBAM attention mechanism.Heliyon · 2024Article
- One-Stage Detection without Segmentation for Multi-Type Coronary Lesions in Angiography Images Using Deep Learning.Diagnostics (Basel, Switzerland) · 2023Article
- Automatic Detection of Microaneurysms in Fundus Images Using an Ensemble-Based Segmentation Method.Sensors (Basel, Switzerland) · 2023Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Diabetic Retinopathy is one of the main causes of vision loss, and in its initial stages, it presents with fundus lesions, such as microaneurysms, hard exudates, hemorrhages, and soft exudates. Computational models capable of detecting these lesions can help in the early diagnosis of the disease and prevent the manifestation of more severe forms of lesions, helping in screening and defining the best form of treatment. However, the detection of these lesions through computerized systems is a challenge due to numerous factors, such as the characteristics of size and shape of the lesions, noise and the contrast of images available in the public datasets of Diabetic Retinopathy, the number of labeled examples of these lesions available in the datasets and the difficulty of deep learning algorithms in detecting very small objects in digital images. Thus, to overcome these problems, this work proposes a new approach based on image processing techniques, data augmentation, transfer learning, and deep neural networks to assist in the medical diagnosis of fundus lesions. The proposed approach was trained, adjusted, and tested using the public DDR and IDRiD Diabetic Retinopathy datasets and implemented in the PyTorch framework based on the YOLOv5 model. The proposed approach reached in the DDR dataset an mAP of 0.2630 for the IoU limit of 0.5 and F1-score of 0.3485 in the validation stage, and an mAP of 0.1540 for the IoU limit of 0.5 and F1-score of 0.2521, in the test stage. The results obtained in the experiments demonstrate that the proposed approach presented superior results to works with the same purpose found in the literature.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.