Importance of Data Augmentation and Transfer Learning on Retinal Vessel Segmentation
Küçük Resim Yok
Tarih
2021
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
IEEE
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
Automatic segmentation of retinal fundus images for extracting blood vessels is an essential task in the diagnostic classification of hypertension, glaucoma, and diabetic retinopathy, which are the leading causes of blindness. In this paper, we employed a transfer learning strategy for improved retinal vessel extraction. Firstly, we trained the U-NET model on CHASE DB1 and DRIVE databases. By using data augmentations on datasets we enable the U-NET model to learn retinal vessel features better. We examined the data augmentation types, namely, pixel-level transformations and affine transformations. Secondly, we utilized the transfer learning approach on two datasets and achieved comparable results with the state-of-the-art studies on retinal vessel segmentation task. Also, we employed combination of affine and pixel-level transformations to further boost segmentation performance.
Açıklama
29th Telecommunications Forum (TELFOR) -- NOV 23-24, 2021 -- ELECTR NETWORK
Anahtar Kelimeler
Retinal Blood Vessel, Segmentation, U-NET, Transfer Learning, Data Augmentation
Kaynak
2021 29th Telecommunications Forum (Telfor)
WoS Q Değeri
N/A
Scopus Q Değeri
N/A
