Automated age-related macular degeneration area estimation -- first results
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Abstract (arXiv)
This work aims to research an automatic method for detecting Age-related Macular Degeneration (AMD) lesions in RGB eye fundus images. For this, we align invasively obtained eye fundus contrast images (the "golden standard" diagnostic) to the RGB ones and use them to hand-annotate the lesions. This is done using our custom-made tool. Using the data, we train and test five different convolutional neural networks: a custom one to classify healthy and AMD-affected eye fundi, and four well-known networks: ResNet50, ResNet101, MobileNetV3, and UNet to segment (localize) the AMD lesions in the affected eye fundus images. We achieve 93.55% accuracy or 69.71% Dice index as the preliminary best results in segmentation with MobileNetV3.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 4 of its 11 references that are in the map. First posted on arXiv 5 Jul 2021.
Builds on
4 of 11 references.
References · 4
- 2019Searching for MobileNetV3cited by 1,994
- 2016An Analysis of Deep Neural Network Models for Practical Applicationscited by 116
- 2019Association of genomic subtypes of lower-grade gliomas with shape features automatically extracted by a deep learning algorithmcited by 77
- 2016Deep learning is effective for the classification of OCT images of normal versus Age-related Macular Degenerationcited by 6
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