WAMDS2: Early detection of wet AMD using Swin Transformer V2.

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...

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Published in:Revista Mexicana de Ingeniería Biomédica Vol. 47; pp. 1 - 24
Main Authors: Márquez Castro, Roberto, Sánchez Cervantes, José Luis, Alor Hernández, Giner, Reyes Delgado, Augusto Javier, Flores Leal, Alfonso, Mancilla Gomez, Martín, Gonzalez Diaz, Jorge Ernesto
Format: Article
Published: Sociedad Mexicana de Ingenieria Biomedica, A.C. 2026 Special Issue
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        10.17488/RMIB.47.SI-TAIH.1520
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        atl: WAMDS2: Early detection of wet AMD using Swin Transformer V2.
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          Márquez Castro, Roberto
          Sánchez Cervantes, José Luis
          Alor Hernández, Giner
          Reyes Delgado, Augusto Javier
          Flores Leal, Alfonso
          Mancilla Gomez, Martín
          Gonzalez Diaz, Jorge Ernesto
        affil:
          I.T. Orizaba, Tecnológico Nacional de México, Orizaba, Veracruz - México
          Facultad de Negocios y Tecnologías, Campus Ixtaczoquitlan, Universidad Veracruzana, Veracruz - México
      su:
        Ophthalmology
        Macular degeneration
        Transformer models
        Computer-aided diagnosis
        Deep learning
        Retinal imaging
        Computer vision
        Early diagnosis
      sug:
        subj:
          Ophthalmology
          Macular degeneration
          Transformer models
          Computer-aided diagnosis
          Deep learning
          Retinal imaging
          Computer vision
          Early diagnosis
      keyword:
        age-related macular degeneration
        swin transformer
        vision transformer
        degeneración macular asociada a la edad
        transformador de visión
        transformador swin
      ab:
        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.
        La degeneración macular asociada con la edad (DMAE) es una enfermedad ocular progresiva que afecta principalmente a personas mayores de 50 años. Entre sus variantes, la DMAE húmeda es la más grave, pues representa la evolución avanzada de la DMAE seca y puede causar una pérdida visual severa si no se detecta a tiempo. Este estudio se centra en el desarrollo de WAMDS2, un módulo web diseñado para identificar características asociadas con la DMAE húmeda, lo que facilita una detección temprana y precisa. Para ello, se llevó a cabo una revisión de literatura sobre la DMAE y técnicas avanzadas de visión por computadora y aprendizaje profundo. El modelo propuesto integra el Swin Transformer V2, un transformador de visión implementado en PyTorch, para analizar imágenes de fondo de ojo y clasificar los diferentes estadios de la enfermedad. El rendimiento del sistema se evaluó mediante métricas como precisión, sensibilidad y F1-Score, logrando una precisión del 84.76 % en el conjunto de prueba, lo que sugiere su viabilidad en entornos clínicos. Los resultados obtenidos resaltan el potencial de WAMDS2 en el ámbito de la oftalmología y la visión por computadora, evidenciando su capacidad para mejorar el diagnóstico automatizado y la atención al paciente.
      pubtype: Academic Journal
      doctype: Article
      src: R
    language: English
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      holder: Sociedad Mexicana de Ingenieria Biomedica, A.C.
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          year: 2026
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