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

5 Jul 2021 · Rokas Pečiulis, Mantas Lukoševičius, Algimantas Kriščiukaitis, Robertas Petrolis, Dovilė Buteikienė ·

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.

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1x1 Convolution • Average Pooling • Batch Normalization • Convolution • Dense Connections • Depthwise Convolution • Depthwise Separable Convolution • Dropout • Global Average Pooling • Hard Swish • Inverted Residual Block • MobileNetV3 • Pointwise Convolution • ReLU • ReLU6 • Sigmoid Activation • Squeeze-and-Excitation Block

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

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