Neural network training method for classifications of diabetic retinopathy image data on matlab
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Tarih
2023
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Altınbaş Üniversitesi / Lisansüstü Eğitim Enstitüsü
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
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.
Açıklama
Anahtar Kelimeler
Convolutional Neural Network, Deep Learning, Image Processing, Hard Exudates, Retinopathy Diabetes, SGD
Kaynak
WoS Q Değeri
Scopus Q Değeri
Cilt
Sayı
Künye
Salih, A. A. A. (2023). Neural network training method for classifications of diabetic retinopathy image data on matlab. (Yayınlanmamış yüksek lisans tezi). Altınbaş Üniversitesi, Lisansüstü Eğitim Enstitüsü, İstanbul.
