ArticleFrontiers in neuroinformatics2021
DR-IIXRN : Detection Algorithm of Diabetic Retinopathy Based on Deep Ensemble Learning and Attention Mechanism.
Article in Frontiers in neuroinformatics, 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.
- Assessing ResNeXt and RegNet Models for Diabetic Retinopathy Classification: A Comprehensive Comparative Study.Diagnostics (Basel, Switzerland) · 2025Article
- A panoramic perspective: application prospects and outlook of multimodal artificial intelligence in the management of diabetic retinopathy.Frontiers in public health · 2025Review
- Enhanced breast cancer diagnosis through integration of computer vision with fusion based joint transfer learning using multi modality medical images.Scientific reports · 2024Article
- A Robust Machine Learning Model for Diabetic Retinopathy Classification.Journal of imaging · 2023Article
- Recent developments on computer aided systems for diagnosis of diabetic retinopathy: a review.Multimedia tools and applications · 2023Article
- GABNet: global attention block for retinal OCT disease classification.Frontiers in neuroscience · 2023Article
- Advances in artificial intelligence applications for ocular surface diseases diagnosis.Frontiers in cell and developmental biology · 2022Review
- Research Progress of Artificial Intelligence Image Analysis in Systemic Disease-Related Ophthalmopathy.Disease markers · 2022Review
- Multi-Model Domain Adaptation for Diabetic Retinopathy Classification.Frontiers in physiology · 2022Article
- Artificial intelligence promotes the diagnosis and screening of diabetic retinopathy.Frontiers in endocrinology · 2022Review
- Detection algorithm for pigmented skin disease based on classifier-level and feature-level fusion.Frontiers in public health · 2022Article
- Attentional Mechanisms and Improved Residual Networks for Diabetic Retinopathy Severity Classification.Journal of healthcare engineering · 2022Article
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6 authors.
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Abstract
Diabetic retinopathy (DR) is one of the common chronic complications of diabetes and the most common blinding eye disease. If not treated in time, it might lead to visual impairment and even blindness in severe cases. Therefore, this article proposes an algorithm for detecting diabetic retinopathy based on deep ensemble learning and attention mechanism. First, image samples were preprocessed and enhanced to obtain high quality image data. Second, in order to improve the adaptability and accuracy of the detection algorithm, we constructed a holistic detection model DR-IIXRN, which consists of Inception V3, InceptionResNet V2, Xception, ResNeXt101, and NASNetLarge. For each base classifier, we modified the network model using transfer learning, fine-tuning, and attention mechanisms to improve its ability to detect DR. Finally, a weighted voting algorithm was used to determine which category (normal, mild, moderate, severe, or proliferative DR) the images belonged to. We also tuned the trained network model on the hospital data, and the real test samples in the hospital also confirmed the advantages of the algorithm in the detection of the diabetic retina. Experiments show that compared with the traditional single network model detection algorithm, the auc, accuracy, and recall rate of the proposed method are improved to 95, 92, and 92%, respectively, which proves the adaptability and correctness of the proposed method.
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