[go: up one dir, main page]

Vij et al., 2023 - Google Patents

A systematic review on diabetic retinopathy detection using deep learning techniques

Vij et al., 2023

Document ID
7536849633958802989
Author
Vij R
Arora S
Publication year
Publication venue
Archives of Computational Methods in Engineering

External Links

Snippet

Segmentation is an essential requirement to accurately access diabetic retinopathy (DR) and it becomes extremely time-consuming and challenging to detect manually. As a result, an automatic retinal fundus image segmentation (RFIS) system is required to precisely …
Continue reading at link.springer.com (other versions)

Classifications

    • 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
    • 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
    • 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
    • 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
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/20—Special algorithmic details
    • G06T2207/20112—Image segmentation details
    • 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
    • G06T2207/20212—Image combination
    • 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
    • G06K9/00597—Acquiring or recognising eyes, e.g. iris verification
    • 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
    • G06K9/00127—Acquiring and recognising microscopic objects, e.g. biological cells and cellular parts
    • 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
    • G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K2209/00—Indexing scheme relating to methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • 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

Similar Documents

Publication Publication Date Title
Vij et al. A systematic review on diabetic retinopathy detection using deep learning techniques
Veena et al. A novel optic disc and optic cup segmentation technique to diagnose glaucoma using deep learning convolutional neural network over retinal fundus images
Imran et al. Comparative analysis of vessel segmentation techniques in retinal images
Xiao et al. Weighted res-unet for high-quality retina vessel segmentation
Li et al. A large-scale database and a CNN model for attention-based glaucoma detection
Khojasteh et al. Fundus images analysis using deep features for detection of exudates, hemorrhages and microaneurysms
Fu et al. Optic disc segmentation by U-net and probability bubble in abnormal fundus images
CN114287878B (en) A method for diabetic retinopathy lesion image recognition based on attention model
Liu et al. A deep learning-based algorithm identifies glaucomatous discs using monoscopic fundus photographs
Dharmawan et al. A new hybrid algorithm for retinal vessels segmentation on fundus images
Melinscak et al. Retinal Vessel Segmentation using Deep Neural Networks.
Fraz et al. An approach to localize the retinal blood vessels using bit planes and centerline detection
Akram et al. Multilayered thresholding-based blood vessel segmentation for screening of diabetic retinopathy
Valizadeh et al. Presentation of a segmentation method for a diabetic retinopathy patient’s fundus region detection using a convolutional neural network
Kumar et al. Analysis of retinal blood vessel segmentation techniques: a systematic survey
Garg et al. A real time cloud-based framework for glaucoma screening using EfficientNet
Suriyasekeran et al. Algorithms for diagnosis of diabetic retinopathy and diabetic macula edema-a review
Panchal et al. ResMU-Net: Residual Multi-kernel U-Net for blood vessel segmentation in retinal fundus images
Guergueb et al. A review of deep learning techniques for glaucoma detection
Saranya et al. Detection of exudates from retinal images for non-proliferative diabetic retinopathy detection using deep learning model
Vij et al. A systematic review on deep learning techniques for diabetic retinopathy segmentation and detection using ocular imaging modalities
Agrawal et al. A survey on automated microaneurysm detection in diabetic retinopathy retinal images
Krishna et al. Convolutional Neural Networks for Automated Diagnosis of Diabetic Retinopathy in Fundus Images
KR20250166186A (en) Retinal image segmentation using semi-supervised learning
Tariq et al. Computer aided diagnostic system for grading of diabetic retinopathy