With the aging population, addressing diseases such as age-related macular degeneration (AMD), which primarily affects people over 50, becomes
increasingly urgent. Several other conditions are associated with it, notably macular edema and choroidal neovascularization, which require early
detection and prompt treatment. These diseases can be identified through Optical Coherence Tomography (OCT) exams. In this study, convolutional
neural networks (CNNs) were trained on a public dataset using various data processing and balancing techniques to compare them and determine the best model for an analytical application, enabling rapid identification and medical referral.
