| Title |
Automated age-related macular degeneration area estimation – first results / |
| Authors |
Pečiulis, Rokas ; Lukoševičius, Mantas ; Kriščiukaitis, Algimantas ; Petrolis, Robertas ; Buteikienė, Dovilė |
| Full Text |
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| Is Part of |
CEUR workshop proceedings: IVUS 2021: Information society and university studies 2021: Proceedings of the 26th international conference on information society and university studies (IVUS 2021), Kaunas, Lithuania, April 23, 2021 / edited by: I. Veitaitė, A. Lopata, T. Krilavičius, M. Woźniak.. Aachen : CEUR-WS. 2021, vol. 2915, art. no. 16, p. 141-149.. ISSN 1613-0073 |
| Keywords [eng] |
age-related macular degeneration ; eye fundus image ; convolutional neural network ; deep learning ; ResNet50 ; ResNet101 ; MobileNetV3 ; UNet |
| Abstract [eng] |
Thiswork aims to research an automatic method for detectingAge-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. |
| Published |
Aachen : CEUR-WS |
| Type |
Conference paper |
| Language |
English |
| Publication date |
2021 |
| CC license |
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