ArticleMedical & biological engineering & computing2022
DCNN-based prediction model for detection of age-related macular degeneration from color fundus images.
Article in Medical & biological engineering & computing, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.
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Who cites it
6 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Vitreoretinal disease detection using artificial intelligence: a systematic review and meta-analysis.International ophthalmology · 2026Pooled it
- OcuViT: A Vision Transformer-Based Approach for Automated Diabetic Retinopathy and AMD Classification.Journal of imaging informatics in medicine · 2026Article
- Topo-CNN: Retinal Image Analysis with Topological Deep Learning.Journal of imaging informatics in medicine · 2026Article
- Automatic diagnosis of age-related macular degeneration using machine learning and image processing techniques.Scientific reports · 2026Article
- Scale-adaptive model for detection and grading of age-related macular degeneration from color retinal fundus images.Scientific reports · 2023Article
- A Novel IoT-Enabled Healthcare Monitoring Framework and Improved Grey Wolf Optimization Algorithm-Based Deep Convolution Neural Network Model for Early Diagnosis of Lung Cancer.Sensors (Basel, Switzerland) · 2023Article
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Authors and funding
2 authors.
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Abstract
Age-related macular degeneration (AMD) is a degenerative disorder in the macular region of the eye. AMD is the leading cause of irreversible vision loss in the elderly population. With the increase in aged population in the world, there is an urgent need to develop low-cost, hassle-free, and portable equipment diagnostic and analytical tools for early diagnosis. As AMD detection is done by examining the fundus images, its diagnosis is heavily dependent on medical personnel and their experience. To remove this issue, computer-aided algorithms may be used for AMD detection. The proposed work offers an effective solution to the AMD detection problem. It proposes a novel 13-layer deep convolutional neural network (DCNN) architecture to screen fundus images to spot direct signs of AMD. Five pairs of convolution and maxpool layers and three fully connected layers are utilized in the proposed network. Extensive simulations on original and augmented versions of two datasets (iChallenge-AMD and ARIA) consisting of healthy and diseased cases show a classification accuracy of 89.75%, 91.69%, and 99.45% on original and augmented versions of iChallenge-AMD and 90.00%, 93.03%, and 99.55% on ARIA, using a 10-fold cross-validation technique. It surpasses the best-known algorithm using DCNN by 2%.
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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.