ArticleDiagnostics (Basel, Switzerland)2022
Automatic Detection of Age-Related Macular Degeneration Based on Deep Learning and Local Outlier Factor Algorithm.
Article in Diagnostics (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 2 of them syntheses that pooled it.
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Who cites it
21 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Performance of Deep Learning in Classifying Age-Related Macular Degeneration From Images: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2026Pooled it
- Pooled it
- TPA-ConvNeXt: Trigonometric Phase Attention for Robust Retinal Disease Classification Across Fundus and OCT.Bioengineering (Basel, Switzerland) · 2026Article
- Long-tailed multi-label retinal disease classification using alternate group training and gradient-based re-weighting.Scientific reports · 2026Article
- Investigating the Impact of Semi-Supervised Learning Methods to Improve the Quality of Diagnosis of Retinal Diseases from OCT Images.Diagnostics (Basel, Switzerland) · 2026Article
- Artificial intelligence based prediction of first recurrence in neovascular age related macular degeneration with validation by 19 experts.Scientific reports · 2026Article
- Automatic diagnosis of age-related macular degeneration using machine learning and image processing techniques.Scientific reports · 2026Article
- The impact of eye movements on amblyopic vision: A mini-review.Vision research · 2025Review
- A Comprehensive CNN Model for Age-Related Macular Degeneration Classification Using OCT: Integrating Inception Modules, SE Blocks, and ConvMixer.Diagnostics (Basel, Switzerland) · 2024Article
- Session-by-Session Prediction of Anti-Endothelial Growth Factor Injection Needs in Neovascular Age-Related Macular Degeneration Using Optical-Coherence-Tomography-Derived Features and Machine Learning.Diagnostics (Basel, Switzerland) · 2024Article
- Recent advances in the application of artificial intelligence in age-related macular degeneration.BMJ open ophthalmology · 2024Review
- Fundus Image Deep Learning Study to Explore the Association of Retinal Morphology with Age-Related Macular Degeneration Polygenic Risk Score.Biomedicines · 2024Article
- Retinal Vein Occlusion-Background Knowledge and Foreground Knowledge Prospects-A Review.Journal of clinical medicine · 2024Review
- A new intelligent system based deep learning to detect DME and AMD in OCT images.International ophthalmology · 2024Article
- Enhancing Readability and Detection of Age-Related Macular Degeneration Using Optical Coherence Tomography Imaging: An AI Approach.Bioengineering (Basel, Switzerland) · 2024Article
- Dry age-related macular degeneration classification from optical coherence tomography images based on ensemble deep learning architecture.Frontiers in medicine · 2024Article
- Intelligent diagnosis of retinal vein occlusion based on color fundus photographs.International journal of ophthalmology · 2024Article
- Wavelet scattering transform application in classification of retinal abnormalities using OCT images.Scientific reports · 2023Article
- Deep Learning Applications to Classification and Detection of Age-Related Macular Degeneration on Optical Coherence Tomography Imaging: A Review.Ophthalmology and therapy · 2023Review
- Research progress on diagnosing retinal vascular diseases based on artificial intelligence and fundus images.Frontiers in cell and developmental biology · 2023Review
Corrections and comments
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Authors and funding
3 authors.
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Abstract
Age-related macular degeneration (AMD) is a retinal disorder affecting the elderly, and society's aging population means that the disease is becoming increasingly prevalent. The vision in patients with early AMD is usually unaffected or nearly normal but central vision may be weakened or even lost if timely treatment is not performed. Therefore, early diagnosis is particularly important to prevent the further exacerbation of AMD. This paper proposed a novel automatic detection method of AMD from optical coherence tomography (OCT) images based on deep learning and a local outlier factor (LOF) algorithm. A ResNet-50 model with L2-constrained softmax loss was retrained to extract features from OCT images and the LOF algorithm was used as the classifier. The proposed method was trained on the UCSD dataset and tested on both the UCSD dataset and Duke dataset, with an accuracy of 99.87% and 97.56%, respectively. Even though the model was only trained on the UCSD dataset, it obtained good detection accuracy when tested on another dataset. Comparison with other methods also indicates the efficiency of the proposed method in detecting AMD.
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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.