WAMDS2: Early detection of wet AMD using Swin Transformer V2. - Márquez Castro, Roberto; Sánchez Cervantes, José Luis; Alor Hernández, Giner; Reyes Delgado, Augusto Javier; Flores Leal, Alfonso; Mancilla Gomez, Martín; Gonzalez Diaz, Jorge Ernesto (Revista Mexicana de Ingeniería Biomédica)
Age-related macular degeneration (AMD) is a progressive eye disease that primarily affects individuals over 50 years old. Among the AMD variants, wet is the most severe, as it represents the advanced stage of dry AMD and can cause severe vision loss if not detected in time. This study focuses on the development of WAMDS2, a web module designed to identify characteristics associated with Wet AMD, facilitating early and accurate detection. To achieve this, a literature review was conducted on AMD and advanced techniques in computer vision and deep learning. The proposed model integrates Swin Transformer V2, a vision transformer implemented in PyTorch, to analyze fundus images and classify the different stages of the disease. The system's performance was evaluated using metrics such as accuracy, recall, and F1-Score. An accuracy of 84.76 % was achieved on the test set, suggesting its feasibility in clinical settings. The obtained results highlight the potential of WAMDS2 in ophthalmology and computer vision, demonstrating its capability to enhance automated diagnosis and patient care.