Automatic Identification of Diabetic Retinopathy in Retinal Images Using Ensemble Learning

• Flávio Araújo UFPI
• Romuere Silva UFPI
• André Macedo UFPI
• Kelson Aires UFPI
• Rodrigo Veras UFPI

Resumo

Diabetic Retinopathy (DR) is one of the major complications of diabetes mellitus and can cause blindness. The diagnosis of DR is performed by visual analysis of retinal images being exudates (fat deposits) the mains patterns traced by a specialist doctor. This paper presents a new method for DR detection in color retinal images. The proposed algorithm combines two images classification methodologies using an ensemble learning. The first Methodology extracts the image attributes using Speeded Up Robust Features (SURF) algorithm and determines the presence of DR using a Support Vector Machine (SVM). The second Methodology consists in the classification of an image based on the segmentation of exudates regions. The experimental validation was performed on a public image database, DIARETDB1.

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