Tesis doctoralsDepartament d'Enginyeria Informàtica i Matemàtiques

Deep Learning-based Methods for Extracting Fundus Image Landmarks and Signs of Eye Diseases

  • Datos identificativos

    Identificador:  TDX:4137
    Autores:  Ali, Mohammed Yousef Salem
    Resumen:
    Diabetic Retinopathy (DR) is the most typical cause of visual loss in working-age adults. In 2040, it is predicted that over 200 million people will have DR. Glaucoma affects about 75 million individuals worldwide, and it is called the silent thief of sight. Therefore, early diagnosis of glaucoma and DR requires an efficient screening procedure. In addition, medical centers regularly perform eye checkups for patients, especially diabetic ones, to minimize the risk of blindness. Examining eye fundus images is labor-intensive, time-consuming, expensive, and error-prone. Therefore, developing a computer-aided diagnosis (CAD) system that analyzes eye fundus images to help ophthalmologists is crucial. Computer vision techniques, like deep learning and especially convolutional neural networks (CNNs), have significantly improved CAD systems' performance. This thesis uses computer vision techniques to consider four eye tasks: optic disc segmentation, glaucoma detection, segmenting exudate lesions and segmenting other kinds of retinal eye DR lesions from fundus images.
  • Otros:

    Editor: Universitat Rovira i Virgili
    Fecha: 2022-12-22, 2023-12-22T23:45:24Z, 2023-01-26T09:25:20Z
    Identificador: http://hdl.handle.net/10803/687502
    Departamento/Instituto: Departament d'Enginyeria Informàtica i Matemàtiques, Universitat Rovira i Virgili.
    Idioma: eng
    Autor: Ali, Mohammed Yousef Salem
    Director: Baget Bernaldiz, Marc, Abdelnasser Mohamed Mahmoud, Mohamed, Valls Mateu, Aïda
    Fuente: TDX (Tesis Doctorals en Xarxa)
    Formato: application/pdf, 147 p.
  • Palabras clave:

    Medical image analysis
    computer vision
    deep learning
    Análisis de imágenes médicas
    visión por computador
    aprendizaje profundo
    Anàlisi d'imatges mèdiques
    visió per computador
    aprenentatge profund
    Enginyeria i arquitectura
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