# Automated age-related macular degeneration area estimation -- first results

arXiv: https://arxiv.org/abs/2107.02211 · HTML: https://mlsanity.com/paper/2107.02211/

## Key facts

- Cited by 1 papers 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 (most cited first, 4 of 4)

- [Searching for MobileNetV3](https://mlsanity.com/paper/1905.02244/) (2019)
- [An Analysis of Deep Neural Network Models for Practical Applications](https://mlsanity.com/paper/1605.07678/) (2016)
- [Association of genomic subtypes of lower-grade gliomas with shape features automatically extracted by a deep learning algorithm](https://mlsanity.com/paper/1906.03720/) (2019)
- [Deep learning is effective for the classification of OCT images of normal versus Age-related Macular Degeneration](https://mlsanity.com/paper/1612.04891/) (2016)

## Built on by (most cited first, 1 of 1)

- [Surpassing state of the art on AMD area estimation from RGB fundus images through careful selection of U-Net architectures and loss functions for class imbalance](https://mlsanity.com/paper/2510.26778/) (2025)

## 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.