An active deep learning method for diabetic retinopathy detection in segmented fundus images using artificial bee colony algorithm
Published 2022 View Full Article
- Home
- Publications
- Publication Search
- Publication Details
Title
An active deep learning method for diabetic retinopathy detection in segmented fundus images using artificial bee colony algorithm
Authors
Keywords
-
Journal
ARTIFICIAL INTELLIGENCE REVIEW
Volume -, Issue -, Pages -
Publisher
Springer Science and Business Media LLC
Online
2022-08-27
DOI
10.1007/s10462-022-10231-3
References
Ask authors/readers for more resources
Related references
Note: Only part of the references are listed.- Applications of Deep Learning in Fundus Images: A Review
- (2021) Tao Li et al. MEDICAL IMAGE ANALYSIS
- Composite deep neural network with gated-attention mechanism for diabetic retinopathy severity classification
- (2021) Jyostna Devi Bodapati et al. Journal of Ambient Intelligence and Humanized Computing
- A new multi-process collaborative architecture for time series classification
- (2021) Zhiwen Xiao et al. KNOWLEDGE-BASED SYSTEMS
- Deep learning architecture based on segmented fundus image features for classification of diabetic retinopathy
- (2021) Sraddha Das et al. Biomedical Signal Processing and Control
- Diabetes and global ageing among 65–99-year-old adults: Findings from the International Diabetes Federation Diabetes Atlas, 9th edition
- (2020) Alan Sinclair et al. DIABETES RESEARCH AND CLINICAL PRACTICE
- Application of deep learning image assessment software VeriSee™ for diabetic retinopathy screening
- (2020) Yi-Ting Hsieh et al. JOURNAL OF THE FORMOSAN MEDICAL ASSOCIATION
- Genetics of diabetes mellitus and diabetes complications
- (2020) Joanne B. Cole et al. Nature Reviews Nephrology
- CABNet: Category Attention Block for Imbalanced Diabetic Retinopathy Grading
- (2020) Along He et al. IEEE TRANSACTIONS ON MEDICAL IMAGING
- Automated identification and grading system of diabetic retinopathy using deep neural networks
- (2019) Wei Zhang et al. KNOWLEDGE-BASED SYSTEMS
- A Data-driven Approach to Referable Diabetic Retinopathy Detection
- (2019) Ramon Pires et al. ARTIFICIAL INTELLIGENCE IN MEDICINE
- Comparative Evaluation of Hand-Crafted Image Descriptors vs. Off-the-Shelf CNN-Based Features for Colour Texture Classification under Ideal and Realistic Conditions
- (2019) Raquel Bello-Cerezo et al. Applied Sciences-Basel
- Referable Diabetic Retinopathy Identification from Eye Fundus Images with Weighted Path for Convolutional Neural Network
- (2019) Yi-Peng Liu et al. ARTIFICIAL INTELLIGENCE IN MEDICINE
- Modified Alexnet architecture for classification of diabetic retinopathy images
- (2019) T. Shanthi et al. COMPUTERS & ELECTRICAL ENGINEERING
- L-Seg: An end-to-end unified framework for multi-lesion segmentation of fundus images
- (2019) Song Guo et al. NEUROCOMPUTING
- Automated diabetic retinopathy grading and lesion detection based on the modified R-FCN object detection algorithm
- (2019) Jianxu Luo et al. IET Computer Vision
- Diagnostic assessment of deep learning algorithms for diabetic retinopathy screening
- (2019) Tao Li et al. INFORMATION SCIENCES
- Diabetic retinopathy detection using red lesion localization and convolutional neural networks
- (2019) Gabriel Tozatto Zago et al. COMPUTERS IN BIOLOGY AND MEDICINE
- IDRiD: Diabetic Retinopathy – Segmentation and Grading Challenge
- (2019) Prasanna Porwal et al. MEDICAL IMAGE ANALYSIS
- A Novel IoT-Perceptive Human Activity Recognition (HAR) Approach Using Multihead Convolutional Attention
- (2019) Haoxi Zhang et al. IEEE Internet of Things Journal
- CANet: Cross-Disease Attention Network for Joint Diabetic Retinopathy and Diabetic Macular Edema Grading
- (2019) Xiaomeng Li et al. IEEE TRANSACTIONS ON MEDICAL IMAGING
- Identifying Medical Diagnoses and Treatable Diseases by Image-Based Deep Learning
- (2018) Daniel S. Kermany et al. CELL
- An ensemble deep learning based approach for red lesion detection in fundus images
- (2018) José Ignacio Orlando et al. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
- Microaneurysm Detection Using Principal Component Analysis and Machine Learning htbp c ab1 *These authors contributed equally.
- (2018) Wen Cao et al. IEEE TRANSACTIONS ON NANOBIOSCIENCE
- Gradation of diabetic retinopathy on reconstructed image using compressed sensing
- (2018) Sudeshna Sil Kar et al. IET Image Processing
- Retinal blood vessel segmentation using the elite-guided multi-objective artificial bee colony algorithm
- (2018) Bilal Khomri et al. IET Image Processing
- Deep convolutional neural networks for diabetic retinopathy detection by image classification
- (2018) Shaohua Wan et al. COMPUTERS & ELECTRICAL ENGINEERING
- An Automated Grading System for Detection of Vision-Threatening Referable Diabetic Retinopathy on the Basis of Color Fundus Photographs
- (2018) Zhixi Li et al. DIABETES CARE
- Automatic recognition of severity level for diagnosis of diabetic retinopathy using deep visual features
- (2017) Qaisar Abbas et al. MEDICAL & BIOLOGICAL ENGINEERING & COMPUTING
- A survey on deep learning in medical image analysis
- (2017) Geert Litjens et al. MEDICAL IMAGE ANALYSIS
- Diabetic retinopathy: global prevalence, major risk factors, screening practices and public health challenges: a review
- (2016) Daniel Shu Wei Ting et al. CLINICAL AND EXPERIMENTAL OPHTHALMOLOGY
- Guest Editorial Deep Learning in Medical Imaging: Overview and Future Promise of an Exciting New Technique
- (2016) Hayit Greenspan et al. IEEE TRANSACTIONS ON MEDICAL IMAGING
- Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs
- (2016) Varun Gulshan et al. JAMA-JOURNAL OF THE AMERICAN MEDICAL ASSOCIATION
- Retinal vessel extraction using Lattice Neural Networks with dendritic processing
- (2015) Roberto Vega et al. COMPUTERS IN BIOLOGY AND MEDICINE
- All-cause mortality in a population-based type 1 diabetes cohort in the U.S. Virgin Islands
- (2014) Raynard E. Washington et al. DIABETES RESEARCH AND CLINICAL PRACTICE
- Active Semi-Supervised Learning Method with Hybrid Deep Belief Networks
- (2014) Shusen Zhou et al. PLoS One
- Computer-aided diagnosis of diabetic retinopathy: A review
- (2013) Muthu Rama Krishnan Mookiah et al. COMPUTERS IN BIOLOGY AND MEDICINE
- Automated Analysis of Diabetic Retinopathy Images: Principles, Recent Developments, and Emerging Trends
- (2013) Baoxin Li et al. Current Diabetes Reports
- Assessing the Need for Referral in Automatic Diabetic Retinopathy Detection
- (2013) Ramon Pires et al. IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING
- TeleOphta: Machine learning and image processing methods for teleophthalmology
- (2013) E. Decencière et al. IRBM
- Detection of New Vessels on the Optic Disc Using Retinal Photographs
- (2011) K A Goatman et al. IEEE TRANSACTIONS ON MEDICAL IMAGING
Find Funding. Review Successful Grants.
Explore over 25,000 new funding opportunities and over 6,000,000 successful grants.
ExploreBecome a Peeref-certified reviewer
The Peeref Institute provides free reviewer training that teaches the core competencies of the academic peer review process.
Get Started