  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.
