ArticleInternational ophthalmology2024
A new intelligent system based deep learning to detect DME and AMD in OCT images.
Article in International ophthalmology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed, 16 citations in OpenAlex.
- Artificial intelligence-driven diagnosis for age-related macular degeneration bridging pathology and engineering: a survey.International ophthalmology · 2025Review
- Residual self-attention vision transformer for detecting acquired vitelliform lesions and age-related macular drusen.Scientific reports · 2025Article
- Toward Efficient Identification of Retinal Diseases: A Lightweight Convolutional Neural Network-Based Approach Using Optical Coherence Tomography.Healthcare technology lettersArticle
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Authors and funding
7 authors at 4 institutions in 2 countries.
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No grant is acknowledged in the PubMed record.
Abstract
Optical Coherence Tomography (OCT) is widely recognized as the leading modality for assessing ocular retinal diseases, playing a crucial role in diagnosing retinopathy while maintaining a non-invasive modality. The increasing volume of OCT images underscores the growing importance of automating image analysis. Age-related diabetic Macular Degeneration (AMD) and Diabetic Macular Edema (DME) are the most common cause of visual impairment. Early detection and timely intervention for diabetes-related conditions are essential for preventing optical complications and reducing the risk of blindness. This study introduces a novel Computer-Aided Diagnosis (CAD) system based on a Convolutional Neural Network (CNN) model, aiming to identify and classify OCT retinal images into AMD, DME, and Normal classes. Leveraging CNN efficiency, including feature learning and classification, various CNN, including pre-trained VGG16, VGG19, Inception_V3, a custom from scratch model, BCNN (VGG16)
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