Artificial intelligence based glaucoma and diabetic retinopathy detection using MATLAB — retrained AlexNet convolutional neural network
2024; Faculty of 1000; Volume: 12; Linguagem: Inglês
10.12688/f1000research.122288.2
ISSN2046-1402
AutoresIsaac Arias-Serrano, Paolo A. Velásquez-López, Laura N. Avila-Briones, Fanny C. Laurido-Mora, Fernando Villalba-Meneses, Andrés Tirado-Espín, Jonathan Cruz-Varela, Diego Almeida-Galárraga,
Tópico(s)Digital Imaging for Blood Diseases
ResumoGlaucoma and diabetic retinopathy (DR) are the leading causes of irreversible retinal damage leading to blindness. Early detection of these diseases through regular screening is especially important to prevent progression. Retinal fundus imaging serves as the principal method for diagnosing glaucoma and DR. Consequently, automated detection of eye diseases represents a significant application of retinal image analysis. Compared with classical diagnostic techniques, image classification by convolutional neural networks (CNN) exhibits potential for effective eye disease detection.
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