Deep convolution neural network for accurate diagnosis of glaucoma using digital fundus images

By U. Raghavendra, Hamido Fujita, S. Bhandary, Anjan Gudigar, Jen Hong Tan, U. R. Acharya. Information Sciences, 2018

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CABNet: Category Attention Block for Imbalanced Diabetic Retinopathy Grading (2020, background): “Thanks to the powerful capability of high-level feature extraction and representational ability of CNNs, they have also been widely used in medical image analysis tasks such as retinal blood vessel segmentation [7], optic disc segmentation [8] and glaucoma screening [9], [10].”
REFUGE Challenge: A Unified Framework for Evaluating Automated Methods for Glaucoma Assessment from Fundus Photographs (2019, positive): “e existing approaches are based on adaptations of standard deep supervised learning techniques, customized to deal with small training sets (Section 2.3). Chen et al. (2015a), Chen et al. (2015b) and Raghavendra et al. (2018) proposed to use shallow architectures with a limited number of layers. Thi”
Evolution of Image Segmentation using Deep Convolutional Neural Network: A Survey (2020, background): “dapt itself according 4 to the predicted result. Among dierent deep learning algorithms, CNN got tremendous success in dierent elds of computer vision as well as grab the area of image segmentation [66, 67, 68]. 3. Semantic Segmentation Semantic segmentation describes the process of associating each”