Haja et al., 2023 - Google Patents
Advancing glaucoma detection with convolutional neural networks: a paradigm shift in ophthalmologyHaja et al., 2023
View HTML- Document ID
- 13030810883504043914
- Author
- Haja S
- Mahadevappa V
- Publication year
- Publication venue
- Romanian Journal of Ophthalmology
External Links
Snippet
A leading cause of irreversible vision loss, glaucoma needs early detection for effective management. Intraocular Pressure (IOP) is a significant risk factor for glaucoma. Convolutional Neural Networks (CNN) demonstrate exceptional capabilities in analyzing …
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
- 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
- G06F19/322—Management of patient personal data, e.g. patient records, conversion of records or privacy aspects
-
- 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
- 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
- G06F19/3487—Medical report generation
-
- 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
- G06F19/36—Computer-assisted acquisition of medical data, e.g. computerised clinical trials or questionnaires
-
- 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
-
- 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
- G06Q50/00—Systems or methods specially adapted for a specific business sector, e.g. utilities or tourism
- G06Q50/10—Services
- G06Q50/22—Health care, e.g. hospitals; Social work
-
- 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
- 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
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Coan et al. | Automatic detection of glaucoma via fundus imaging and artificial intelligence: A review | |
Menaouer et al. | Diabetic retinopathy classification using hybrid deep learning approach | |
EP3944185A1 (en) | Computer-implemented method, system and computer program product for detecting a retinal condition from eye fundus images | |
Norouzifard et al. | Automated glaucoma diagnosis using deep and transfer learning: Proposal of a system for clinical testing | |
Haja et al. | Advancing glaucoma detection with convolutional neural networks: a paradigm shift in ophthalmology | |
Karthiyayini et al. | Retinal image analysis for ocular disease prediction using rule mining algorithms | |
Bao et al. | Current application of digital diagnosing systems for retinopathy of prematurity | |
Bali et al. | Analysis of deep learning techniques for prediction of eye diseases: A systematic review | |
Shamrat et al. | Analysing most efficient deep learning model to detect COVID-19 from computer tomography images | |
Vimala et al. | Diagnosis of diabetic retinopathy by extracting blood vessels and exudates using retinal color fundus images | |
George et al. | A two-stage CNN model for the classification and severity analysis of retinal and choroidal diseases in OCT images | |
Chakravadhanula | A smartphone-based test and predictive models for rapid, non-invasive, and point-of-care monitoring of ocular and cardiovascular complications related to diabetes | |
Sridhar et al. | Artificial intelligence in medicine: diabetes as a model | |
CN111436212A (en) | Application of deep learning for medical imaging assessment | |
Sinha et al. | Transfer learning-based detection of retina damage from optical coherence tomography images | |
GALAGAN et al. | Automation of polycystic ovary syndrome diagnostics through machine learning algorithms in ultrasound imaging | |
Mani et al. | An automated hybrid decoupled convolutional network for laceration segmentation and grading of retinal diseases using optical coherence tomography (OCT) images | |
Pravallika et al. | Machine Learning for Early Detection of Diabetic Retinopathy: Leveraging DenseNet CNNs | |
Vora et al. | Early Diagnosis of Cataract and Diabetic Retinopathy for Rural India using a Cloud-Based Deep Learning Model | |
Alsadoun et al. | Artificial Intelligence (AI)-Enhanced Detection of Diabetic Retinopathy From Fundus Images: The Current Landscape and Future Directions | |
Chuntranapaporn et al. | Ocular Duction Measurement Using Three Convolutional Neural Network Models: A Comparative Study | |
Anusuya et al. | A Comprehensive Review of Glaucoma Detection from Fundus Images using Deep Learning | |
Merlin et al. | An Experimental Analysis Based on Automated Detection of Polycystic Ovary Syndrome on Ultrasound Image using Deep Learning Models | |
Gajaram | An Approach to Classify Ocular diseases using Machine Learning and Deep Learning | |
Zaribovna et al. | Medical Image Processing and Deep Learning Models and Algorithms.(For Eye Diseases) |