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    Neural network training method for classifications of diabetic retinopathy image data on matlab
    (Altınbaş Üniversitesi / Lisansüstü Eğitim Enstitüsü, 2023) Salih, Ahmed Afeef Ameen; Kurnaz Türkben, Ayça
    Deep learning was used in this research to develop a method that could identify hard exudates in DR fundus pictures. Patients with diabetic retinopathy need to be able to differentiate between hard exudates and other symptoms of sickness. This innovative technique accurately identifies hard exudates 99.7% of the time. In the future, detection will also include blood flow and microaneurysms in addition to soft exudates. In addition to this, we need to quantify DR by utilizing segmented pictures. There are two approaches that improve model precision. Then, change the dimensions of the picture patch. The next thing that we could do is investigate the role that picture patches have in the accuracy of the forecast. Convolutional neural networks are utilized in the second technique, which performs an analysis on the first and last 16 unpredicted pixels in each row and column.