Hierarchical retinal blood vessel segmentation based on feature and ensemble learning

By Shuangling Wang, Yilong Yin, Guibao Cao, Benzheng Wei, Yuanjie Zheng, Gongping Yang. Neurocomputing, 2015

380 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Ophthalmology, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/10c9baf32cfec7db9f1ad1638f1f0b269acd7004

Citations in the corpus

• Computer Methods and Programs in Biomedicine (neutral): “The extracted features are then classified with SVM. Similarly, in [56] , CNNs are used to extract hierarchical features from retinal color fundus images, which are then classified with ensemble RF.”
• DUNet: A deformable network for retinal vessel segmentation (2018, neutral): “two superior classifiers, Convolutional Neural Network (CNN) and Random Forest (RF) together to carry out the segmentation [27].”