An efficient deep neural architecture for detecting different stages of age-related macular degeneration

Abstract

Age-related macular degeneration (AMD) is a leading cause of vision loss in the elderly population, characterized by progressive retinal degeneration that advances through distinct stages: early, intermediate, and late (advanced). Clinical management, early intervention, and prevention of irreversible visual impairment require proper and timely diagnosis of these stages. Nevertheless, the manual interpretation of retinal images is still laborious and prone to interobserver error, which demonstrates the necessity of the use of high-quality automated diagnostic systems. Our work in this paper suggests an effective deep neural network to identify and classify the stages of AMD with the use of retinal imaging, namely, optical coherence tomography (OCT). The research first performs a binary detection to make the distinction between AMD and Non-AMD cases. We then categorise the cases of detected AMD on the basis of early, intermediate and late stages. The framework proposed makes use of transfer learning using various state-of-the-art convolutional neural network (CNN) backbones, such as ResNet50, EfficientNetB4, ConvNeXt Small, ConvNeXt Tiny, and EfficientNetV2_L that have been selected due to their effective representational ability and efficient computing. These are fine-tuned models that are used to observe subtle pathological changes related to drusen accumulation, pigmentary defects, geographic atrophy, and neovascularization throughout the disease range. The architecture assumes the combination of the standardized preprocessing, efficient training methods, and the relative assessment of the chosen networks to find an effective and resource-efficient model of the multi-class AMD staging. Experimental findings demonstrate good performance. ConvNeXt Small reaches 97.9% accuracy in detection and 83.5% in staging. It outperforms ResNet50 (97.5% detection, 77.4% staging), EfficientNetB4 (97.5% detection, 69.9% staging), ConvNeXt Tiny (96.7% detection, 78.2% staging), and EfficientNetV2_L (96.7% detection, 80.4% staging). All models improve early-stage sensitivity, with recalls above 86%. Experimental results demonstrate that the proposed approach achieves robust performance across all disease stages, with improved sensitivity in early-stage detection. This work highlights the potential of modern deep learning models as scalable decision-support tools for large-scale screening and clinical diagnosis, contributing to improved outcomes in the management of age-related macular degeneration.