[go: up one dir, main page]
More Web Proxy on the site http://driver.im/

Khalid et al., 2024 - Google Patents

FGR-Net: interpretable fundus image gradeability classification based on deep reconstruction learning

Khalid et al., 2024

View HTML
Document ID
10857266952519702237
Author
Khalid S
Rashwan H
Abdulwahab S
Abdel-Nasser M
Quiroga F
Puig D
Publication year
Publication venue
Expert Systems With Applications

External Links

Snippet

The performance of diagnostic Computer-Aided Design (CAD) systems for retinal diseases depends on the quality of the retinal images being screened. Thus, many studies have been developed to evaluate and assess the quality of such retinal images. However, most of them …
Continue reading at www.sciencedirect.com (HTML) (other versions)

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F19/00—Digital computing or data processing equipment or methods, specially adapted for specific applications
    • G06F19/30—Medical informatics, i.e. computer-based analysis or dissemination of patient or disease data
    • G06F19/34—Computer-assisted medical diagnosis or treatment, e.g. computerised prescription or delivery of medication or diets, computerised local control of medical devices, medical expert systems or telemedicine
    • G06F19/345—Medical expert systems, neural networks or other automated diagnosis
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/0002—Inspection of images, e.g. flaw detection
    • G06T7/0012—Biomedical image inspection
    • G06T7/0014—Biomedical image inspection using an image reference approach
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/30—Subject of image; Context of image processing
    • G06T2207/30004—Biomedical image processing
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/10—Image acquisition modality
    • G06T2207/10024—Color image
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/62—Methods or arrangements for recognition using electronic means
    • G06K9/6217—Design or setup of recognition systems and techniques; Extraction of features in feature space; Clustering techniques; Blind source separation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F19/00—Digital computing or data processing equipment or methods, specially adapted for specific applications
    • G06F19/30—Medical informatics, i.e. computer-based analysis or dissemination of patient or disease data
    • G06F19/32—Medical data management, e.g. systems or protocols for archival or communication of medical images, computerised patient records or computerised general medical references
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/20—Special algorithmic details
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/62—Methods or arrangements for recognition using electronic means
    • G06K9/6267—Classification techniques
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/36—Image preprocessing, i.e. processing the image information without deciding about the identity of the image
    • G06K9/46—Extraction of features or characteristics of the image
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T5/00—Image enhancement or restoration, e.g. from bit-mapped to bit-mapped creating a similar image
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T11/00—2D [Two Dimensional] image generation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06Q—DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q40/00—Finance; Insurance; Tax strategies; Processing of corporate or income taxes

Similar Documents

Publication Publication Date Title
Tsiknakis et al. Deep learning for diabetic retinopathy detection and classification based on fundus images: A review
Qureshi et al. Diabetic retinopathy detection and stage classification in eye fundus images using active deep learning
Xu et al. FFU‐Net: Feature Fusion U‐Net for Lesion Segmentation of Diabetic Retinopathy
Raj et al. Fundus image quality assessment: survey, challenges, and future scope
EP4057215A1 (en) Systems and methods for automated analysis of retinal images
Lin et al. Development of revised ResNet-50 for diabetic retinopathy detection
Shorfuzzaman et al. An explainable deep learning ensemble model for robust diagnosis of diabetic retinopathy grading
Navarro et al. Automatic detection of microaneurysms in diabetic retinopathy fundus images using the L* a* b color space
Skouta et al. Deep learning for diabetic retinopathy assessments: a literature review
Yang et al. RADCU-Net: Residual attention and dual-supervision cascaded U-Net for retinal blood vessel segmentation
Shamrat et al. An advanced deep neural network for fundus image analysis and enhancing diabetic retinopathy detection
Bali et al. Analysis of deep learning techniques for prediction of eye diseases: A systematic review
Kumar et al. Deep learning-assisted retinopathy of prematurity (ROP) screening
Dubey et al. Recent developments on computer aided systems for diagnosis of diabetic retinopathy: a review
Wang et al. Automatic vessel crossing and bifurcation detection based on multi-attention network vessel segmentation and directed graph search
Xu et al. A deep retinal image quality assessment network with salient structure priors
WO2014158345A1 (en) Methods and systems for vessel bifurcation detection
Lin et al. Blu-gan: Bi-directional convlstm u-net with generative adversarial training for retinal vessel segmentation
Khalid et al. FGR-Net: interpretable fundus image gradeability classification based on deep reconstruction learning
Ashtari-Majlan et al. Deep learning and computer vision for glaucoma detection: A review
Mukherjee et al. Application of deep learning approaches for classification of diabetic retinopathy stages from fundus retinal images: a survey
Zhao et al. Automated detection of vessel abnormalities on fluorescein angiogram in malarial retinopathy
Dayana et al. Feature fusion and optimization integrated refined deep residual network for diabetic retinopathy severity classification using fundus image
Lei et al. GNN-fused CapsNet with multi-head prediction for diabetic retinopathy grading
Durai et al. Automatic severity grade classification of diabetic retinopathy using deformable ladder Bi attention U-net and deep adaptive CNN