ArticleBiomedical engineering online2017
Machine learning techniques for diabetic macular edema (DME) classification on SD-OCT images.
Article in Biomedical engineering online, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 37 papers, 1 of them a synthesis that pooled it.
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Who cites it
37 citing papers in PubMed, 1 synthesis or guideline pooled it.
- In-depth analysis of research hotspots and emerging trends in AI for retinal diseases over the past decade.Frontiers in medicine · 2024Pooled it
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- WaveAttention-ResNet: a deep learning-based intelligent diagnostic model for the auxiliary diagnosis of multiple retinal diseases.Frontiers in radiology · 2025Article
- A deep learning approach to hard exudates detection and disorganization of retinal inner layers identification on OCT images.Scientific reports · 2024Article
- Automated deep learning-based AMD detection and staging in real-world OCT datasets (PINNACLE study report 5).Scientific reports · 2023Article
- Artificial intelligence in retinal disease: clinical application, challenges, and future directions.Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie · 2023Review
- Detection and Classification of Diabetic Macular Edema with a Desktop-Based Code-Free Machine Learning Tool.Turkish journal of ophthalmology · 2023Article
- Recurrent Self Fusion: Iterative Denoising for Consistent Retinal OCT Segmentation.Ophthalmic medical image analysis : 10th International Workshop, OMIA 2023, held in conjunction with MICCAI 2023, Vancouver, BC, Canada, October 12, 2023, Proceedings. OMIA (Workshop) (10th : 2023 : Vancouver, B.C. ; Online) · 2023Article
- Automated Age-Related Macular Degeneration Detector on Optical Coherence Tomography Images Using Slice-Sum Local Binary Patterns and Support Vector Machine.Sensors (Basel, Switzerland) · 2023Article
- Retinal Disease Detection Using Deep Learning Techniques: A Comprehensive Review.Journal of imaging · 2023Review
- Classification of Retinal Diseases in Optical Coherence Tomography Images Using Artificial Intelligence and Firefly Algorithm.Diagnostics (Basel, Switzerland) · 2023Article
- A Deep Learning-Based Framework for Retinal Disease Classification.Healthcare (Basel, Switzerland) · 2023Article
- Artificial intelligence method based on multi-feature fusion for automatic macular edema (ME) classification on spectral-domain optical coherence tomography (SD-OCT) images.Frontiers in neuroscience · 2023Article
- Optical coherence tomography image based eye disease detection using deep convolutional neural network.Health information science and systems · 2022Article
- Classification of oral salivary gland tumors based on texture features in optical coherence tomography images.Lasers in medical science · 2022Article
- A Novel Computer-Aided Diagnostic System for Early Detection of Diabetic Retinopathy Using 3D-OCT Higher-Order Spatial Appearance Model.Diagnostics (Basel, Switzerland) · 2022Article
- Ophthalmology and Artificial Intelligence: Present or Future? A Diabetic Retinopathy Screening Perspective of the Pursuit for Fairness.Frontiers in ophthalmology · 2022Article
- Artificial intelligence promotes the diagnosis and screening of diabetic retinopathy.Frontiers in endocrinology · 2022Review
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6 authors.
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Abstract
backgroundSpectral domain optical coherence tomography (OCT) (SD-OCT) is most widely imaging equipment used in ophthalmology to detect diabetic macular edema (DME). Indeed, it offers an accurate visualization of the morphology of the retina as well as the retina layers.
methodsThe dataset used in this study has been acquired by the Singapore Eye Research Institute (SERI), using CIRRUS TM (Carl Zeiss Meditec, Inc., Dublin, CA, USA) SD-OCT device. The dataset consists of 32 OCT volumes (16 DME and 16 normal cases). Each volume contains 128 B-scans with resolution of 1024 px × 512 px, resulting in more than 3800 images being processed. All SD-OCT volumes are read and assessed by trained graders and identified as normal or DME cases based on evaluation of retinal thickening, hard exudates, intraretinal cystoid space formation, and subretinal fluid. Within the DME sub-set, a large number of lesions has been selected to create a rather complete and diverse DME dataset. This paper presents an automatic classification framework for SD-OCT volumes in order to identify DME versus normal volumes. In this regard, a generic pipeline including pre-processing, feature detection, feature representation, and classification was investigated. More precisely, extraction of histogram of oriented gradients and local binary pattern (LBP) features within a multiresolution approach is used as well as principal component analysis (PCA) and bag of words (BoW) representations. RESULTS AND
conclusionBesides comparing individual and combined features, different representation approaches and different classifiers are evaluated. The best results are obtained for LBP[Formula: see text] vectors while represented and classified using PCA and a linear-support vector machine (SVM), leading to a sensitivity(SE) and specificity (SP) of 87.5 and 87.5%, respectively.
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