Kumar et al., 2020 - Google Patents
Drdnet: Diagnosis of diabetic retinopathy using capsule network (workshop paper)Kumar et al., 2020
- Document ID
- 3530073440731454274
- Author
- Kumar G
- Chatterjee S
- Chattopadhyay C
- Publication year
- Publication venue
- 2020 IEEE Sixth International Conference on Multimedia Big Data (BigMM)
External Links
Snippet
Diabetic Retinopathy (DR) is a polygenic disorder issue that affects human eyes. Bruise to the blood vessels of the photosensitive tissue of the retina causes this complication. It's most frequent in patients who had diabetes for more than ten years. This downside is going on in …
- 206010012689 Diabetic retinopathy 0 title abstract description 48
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
- G06K9/6217—Design or setup of recognition systems and techniques; Extraction of features in feature space; Clustering techniques; Blind source separation
- G06K9/6256—Obtaining sets of training patterns; Bootstrap methods, e.g. bagging, boosting
-
- 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
- G06K9/6267—Classification techniques
- G06K9/6279—Classification techniques relating to the number of classes
-
- 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
- G06K9/68—Methods or arrangements for recognition using electronic means using sequential comparisons of the image signals with a plurality of references in which the sequence of the image signals or the references is relevant, e.g. addressable memory
-
- 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
-
- 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
- G06K9/00597—Acquiring or recognising eyes, e.g. iris verification
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N99/00—Subject matter not provided for in other groups of this subclass
- G06N99/005—Learning machines, i.e. computer in which a programme is changed according to experience gained by the machine itself during a complete run
-
- 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
- G06K9/03—Detection or correction of errors, e.g. by rescanning the pattern
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computer systems based on biological models
- G06N3/02—Computer systems based on biological models using neural network models
- G06N3/08—Learning methods
- G06N3/082—Learning methods modifying the architecture, e.g. adding or deleting nodes or connections, pruning
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Ghosh et al. | Automatic detection and classification of diabetic retinopathy stages using CNN | |
Chakrabarty | A deep learning method for the detection of diabetic retinopathy | |
Soomro et al. | Strided fully convolutional neural network for boosting the sensitivity of retinal blood vessels segmentation | |
Zhou et al. | Multi-cell multi-task convolutional neural networks for diabetic retinopathy grading | |
Kumar et al. | Drdnet: Diagnosis of diabetic retinopathy using capsule network (workshop paper) | |
CN110013216B (en) | Artificial intelligence cataract analysis system | |
Barhate et al. | Reducing overfitting in diabetic retinopathy detection using transfer learning | |
Shenavarmasouleh et al. | Drdr ii: Detecting the severity level of diabetic retinopathy using mask rcnn and transfer learning | |
Bali et al. | Transfer learning-based one versus rest classifier for multiclass multi-label ophthalmological disease prediction | |
Benson et al. | Transfer learning for diabetic retinopathy | |
Baba et al. | Automated diabetic retinopathy severity grading using novel DR-ResNet+ deep learning model | |
Thanati et al. | On deep learning based algorithms for detection of diabetic retinopathy | |
Kolte et al. | Advancing Diabetic Retinopathy Detection: Leveraging Deep Learning for Accurate Classification and Early Diagnosis | |
Calderon et al. | CNN-based quality assessment for retinal image captured by wide field of view non-mydriatic fundus camera | |
Swain et al. | Diabetic retinopathy using image processing and deep learning | |
Naveenkumar et al. | Diabetic Retinopathy Disease Classification Using EfficientNet-B3 | |
Alawi et al. | Parasitized cell recognition using alexnet pre-trained model | |
Santos et al. | Generating photorealistic images of people's eyes with strabismus using Deep Convolutional Generative Adversarial Networks | |
Singh et al. | A Deep Learning Approach to Analyze Diabetic Retinopathy Lesions using Scant Data | |
Karthik et al. | Design and Implementation of Multi-Retinal Disease Classification Using Deep Neural Network | |
Nandhini et al. | An automated detection and multi-stage classification of diabetic retinopathy using convolutional neural networks | |
Sarki | Automatic detection of diabetic eye disease through deep learning using fundus images | |
Zhong et al. | CeCNN: Copula-enhanced convolutional neural networks in joint prediction of refraction error and axial length based on ultra-widefield fundus images | |
Saranya et al. | Deep learning based algorithm for detection of diabetic retinopathy | |
Paul et al. | Blindness risk prediction caused by diabetic retinopathy from retinal image |