Simultaneous multiclass retinal lesion segmentation using fully automated RILBP-YNet in diabetic retinopathy
Published 2023 View Full Article
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Title
Simultaneous multiclass retinal lesion segmentation using fully automated RILBP-YNet in diabetic retinopathy
Authors
Keywords
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Journal
Biomedical Signal Processing and Control
Volume 86, Issue -, Pages 105205
Publisher
Elsevier BV
Online
2023-06-28
DOI
10.1016/j.bspc.2023.105205
References
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Related references
Note: Only part of the references are listed.- CARNet: Cascade attentive RefineNet for multi-lesion segmentation of diabetic retinopathy images
- (2022) Yanfei Guo et al. Complex & Intelligent Systems
- Robust segmentation of exudates from retinal surface using M-CapsNet via EM routing
- (2021) B. Biswal et al. Biomedical Signal Processing and Control
- Deep Bayesian baseline for segmenting diabetic retinopathy lesions: Advances and challenges
- (2021) Azat Garifullin et al. COMPUTERS IN BIOLOGY AND MEDICINE
- NFN+: A novel network followed network for retinal vessel segmentation
- (2020) Yicheng Wu et al. NEURAL NETWORKS
- Multi-Path Recurrent U-Net Segmentation of Retinal Fundus Image
- (2020) Yun Jiang et al. Applied Sciences-Basel
- Controlled differential evolution based detection of neovascularization on optic disc using support vector machine
- (2020) Birendra Biswal et al. Biomedical Engineering-Biomedizinische Technik
- CAB U-Net: An end-to-end category attention boosting algorithm for segmentation
- (2020) Xiaofeng Ding et al. COMPUTERIZED MEDICAL IMAGING AND GRAPHICS
- Deep membrane systems for multitask segmentation in diabetic retinopathy
- (2019) Jie Xue et al. KNOWLEDGE-BASED SYSTEMS
- L-Seg: An end-to-end unified framework for multi-lesion segmentation of fundus images
- (2019) Song Guo et al. NEUROCOMPUTING
- IDRiD: Diabetic Retinopathy – Segmentation and Grading Challenge
- (2019) Prasanna Porwal et al. MEDICAL IMAGE ANALYSIS
- Segmenting Diabetic Retinopathy Lesions in Multispectral Images Using Low-Dimensional Spatial-Spectral Matrix Representation
- (2019) Yunlong He et al. IEEE Journal of Biomedical and Health Informatics
- DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs
- (2018) Liang-Chieh Chen et al. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
- Robust retinal blood vessel segmentation using line detectors with multiple masks
- (2018) Birendra Biswal et al. IET Image Processing
- Exudate-based diabetic macular edema recognition in retinal images using cascaded deep residual networks
- (2018) Juan Mo et al. NEUROCOMPUTING
- Robust retinal blood vessel segmentation using hybrid active contour model
- (2018) Prakash Kumar Karn et al. IET Image Processing
- SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation
- (2017) Vijay Badrinarayanan et al. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
- Fully Convolutional Networks for Semantic Segmentation
- (2017) Evan Shelhamer et al. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
- Automated segmentation of exudates, haemorrhages, microaneurysms using single convolutional neural network
- (2017) Jen Hong Tan et al. INFORMATION SCIENCES
- Retinal Disease Screening Through Local Binary Patterns
- (2017) Sandra Morales et al. IEEE Journal of Biomedical and Health Informatics
- Deep learning
- (2015) Yann LeCun et al. NATURE
- Automated detection of proliferative diabetic retinopathy using a modified line operator and dual classification
- (2014) R.A. Welikala et al. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
- DREAM: Diabetic Retinopathy Analysis Using Machine Learning
- (2014) Sohini Roychowdhury et al. IEEE Journal of Biomedical and Health Informatics
- Automatic Exudate Detection from Non-dilated Diabetic Retinopathy Retinal Images Using Fuzzy C-means Clustering
- (2009) Akara Sopharak et al. SENSORS
- Automatic detection of diabetic retinopathy exudates from non-dilated retinal images using mathematical morphology methods
- (2008) Akara Sopharak et al. COMPUTERIZED MEDICAL IMAGING AND GRAPHICS
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