Nothing Special   »   [go: up one dir, main page]

Rebinth et al., 2019 - Google Patents

A deep learning approach to computer aided glaucoma diagnosis

Rebinth et al., 2019

View PDF
Document ID
3196688193431181849
Author
Rebinth A
Kumar S
Publication year
Publication venue
2019 International Conference on Recent Advances in Energy-efficient Computing and Communication (ICRAECC)

External Links

Snippet

Glaucoma has been listed as a major health deterrent and is one of the top three causes of vision loss which may lead to permanent blindness. Recent global health evaluation on primary health challenges conducted by World Health Organization (WHO) has identified …
Continue reading at www.researchgate.net (PDF) (other versions)

Classifications

    • 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
    • G06K9/6268—Classification techniques relating to the classification paradigm, e.g. parametric or non-parametric approaches
    • 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
    • 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
    • G06K9/00597—Acquiring or recognising eyes, e.g. iris verification
    • G06K9/0061—Preprocessing; Feature extraction
    • 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
    • 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
    • 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
    • G06K9/00221—Acquiring or recognising human faces, facial parts, facial sketches, facial expressions
    • G06K9/00268—Feature extraction; Face representation
    • G06K9/00281—Local features and components; Facial parts ; Occluding parts, e.g. glasses; Geometrical relationships
    • 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
    • G06K9/00362—Recognising human body or animal bodies, e.g. vehicle occupant, pedestrian; Recognising body parts, e.g. hand
    • 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
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Detecting, measuring or recording for diagnostic purposes; Identification of persons
    • A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
    • A61B5/7235—Details of waveform analysis
    • A61B5/7264—Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems

Similar Documents

Publication Publication Date Title
Salamat et al. Diabetic retinopathy techniques in retinal images: A review
Fraz et al. Multiscale segmentation of exudates in retinal images using contextual cues and ensemble classification
Akram et al. Automated detection of exudates and macula for grading of diabetic macular edema
Saba et al. Fundus image classification methods for the detection of glaucoma: A review
Singh et al. Image processing based automatic diagnosis of glaucoma using wavelet features of segmented optic disc from fundus image
Acharya et al. Decision support system for the glaucoma using Gabor transformation
Akram et al. Detection and classification of retinal lesions for grading of diabetic retinopathy
Mookiah et al. Data mining technique for automated diagnosis of glaucoma using higher order spectra and wavelet energy features
Akram et al. Detection of neovascularization in retinal images using multivariate m-Mediods based classifier
Harangi et al. Automatic exudate detection by fusing multiple active contours and regionwise classification
Mookiah et al. Local configuration pattern features for age-related macular degeneration characterization and classification
Pathan et al. Automated segmentation and classifcation of retinal features for glaucoma diagnosis
Roychowdhury et al. Automated detection of neovascularization for proliferative diabetic retinopathy screening
AbdelMaksoud et al. A comprehensive diagnosis system for early signs and different diabetic retinopathy grades using fundus retinal images based on pathological changes detection
Malhi et al. Detection and diabetic retinopathy grading using digital retinal images
Sakthivel et al. An automated detection of glaucoma using histogram features
Rebinth et al. A deep learning approach to computer aided glaucoma diagnosis
Manjramkar Survey of diabetic retinopathy screening methods
Devaraj et al. A survey on segmentation of exudates and microaneurysms for early detection of diabetic retinopathy
Farooq et al. Improved automatic localization of optic disc in Retinal Fundus using image enhancement techniques and SVM
Indumathi et al. Microaneurysms detection for early diagnosis of diabetic retinopathy using shape and steerable Gaussian features
Sonti et al. Shape and texture based identification of glaucoma from retinal fundus images
Ruba et al. Identification and segmentation of exudates using SVM classifier
Raveenthini et al. Multiocular disease detection using a generic framework based on handcrafted and deep learned feature analysis
Giraddi et al. Role of GLCM features in identifying abnormalities in the retinal images