An AMDOCT-NET for Automated AMD Detection under Evaluations of Different Image Size, Denoising and Cropping

This paper proposed a novel deep learning architecture, called the AMDOCT-NET architecture, to accurately detect age-related macular degeneration (AMD) on optical coherence tomography (OCT) images. Using the AMDOCT-NET architecture, the performance of various image processing, such as resizing, denoising, and cropping has been evaluated. The simulation results show that the AMDOCT-NET architecture with an input size of 224×224 pixels, no cropping, and no denoising achieves the accuracy of 99.09% to automatically detect the AMD. Compared with the well-known deep learning architecture, VGG16, the AMDOCT-NET improves accuracy by 2.09% and reduces the model size by 53.7%.