Age-Related Retinal Disease Identification Using Image Processing and Deep Learning

The research employs a novel integration of image processing and deep learning technologies to improve the diagnostics of age-related retinal diseases, namely Diabetic Macular Edema (DME), Choroidal Neovascularization (CNV), Age-Related Macular Degeneration (ARMD), and Diabetic Retinopathy (DR). It presents an advanced architecture that seeks to enhance the accessibility and effectiveness of diagnostic processes worldwide, in addition to automating the identification of these disorders using a variety of datasets from well-known sites like Kaggle and leveraging the capabilities of state-of-the-art programs like TensorFlow and Microsoft Azure. To obtain a high degree of accuracy in early disease identification, the methodology combines custom and pre-trained deep learning models in a hybrid approach. The preliminary findings highlight the capacity of the approach to considerably reduce the probability of visual impairment using early intervention. The research is a multifaceted effort to improve the infrastructure of global eye healthcare by developing diagnostic technologies that are scalable, dependable, and easy to use.