Abstract
Background: Age-related macular degeneration (AMD) is one of the most severe vision-threatening diseases, and yet Fundus Fluorescein Angiography (FFA) is the gold standard for AMD diagnosis. In recent years, many AMD computer-aided diagnosis (CAD) systems have been developed based on either color fundus images or OCT images. However, there is no CAD technique that integrates FFA with other ophthalmic imaging so far. Methods: In order to improve the performance of AMD CAD system, we propose a pioneering CAD pipeline that combines color fundus and FFA photography. This novel pipeline is the first work that incorporates FFA with any other modality. Six deep neural networks (ResNet-18, ResNet-50, ResNet-101, Inception-V3, Inception-ResNetV2, and DenseNet-201) were utilized to extract feature vectors to facilitate five classifiers (Random Forest, K-Nearest Neighbor, and Support Vector Machine with Linear, Gaussian, and Quadratic functions) for AMD diagnosis. The pipeline was validated on 664 pairs of color fundus and FFA images using 10-fold cross-validation. Results and conclusion: The accuracy and area under curve (AUC) value achieves 93.8% and 0.97, respectively. The results demonstrate that combining color fundus images and FFA images in CAD system is beneficial for AMD diagnosis, indicating promising potential to clinical practice in the future.
Automated Grading of Age-Related Macular Degeneration From Color Fundus Images Using Deep Convolutional Neural Networks
