| Predicate |
Object |
| assignee |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentassignee/MD5_07429dec5d60fd13ee0a25c6c3ab9180 |
| classificationCPCAdditional |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06T2207-30041
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06T2207-10101
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06T2207-20084
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06T2207-20081 |
| classificationCPCInventive |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06T7-0012
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06N3-08
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06N3-045
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06N3-047
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06T5-007
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06T5-002 |
| classificationIPCInventive |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06N3-04
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06T7-00
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06T5-00
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06N3-08 |
| filingDate |
2022-03-23-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| inventor |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_56524120a9c500880f9b2426c3043e41
http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_9d91d90d9d35ca1db34fb0f20a6fcbdd
http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_8cff67ddde1cf1c95f414c645d664d79
http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_b4c3cff6b4afc5eab04f2af09f2fc7a8 |
| publicationDate |
2022-06-14-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| publicationNumber |
CN-114627091-A |
| titleOfInvention |
Retinal age recognition method and device |
| abstract |
The present application relates to a retinal age recognition method and device. The method includes: building a convolutional neural network model, adding an attention mechanism module to the model; preprocessing the image to be recognized to obtain a canonical image; inputting the canonical image into the building Train in a good model; predict retinal age through the trained model; visualize the attention mechanism to obtain the areas that receive the most attention from the model. The scheme of the present application uses a neural network model to identify the retinal age of the test subject, can process big data, and overcome the shortcomings of traditional methods that are complicated in steps and cannot process big data; and does not require manual feature selection, and the network can automatically extract features; The attention mechanism is added to improve the accuracy of retinal age prediction; finally, the results are visualized, and it can be observed which part of the retina is more concerned by the convolutional neural network, which also promotes the manual identification of medical workers. |
| isCitedBy |
http://rdf.ncbi.nlm.nih.gov/pubchem/patent/CN-115171204-A
http://rdf.ncbi.nlm.nih.gov/pubchem/patent/CN-115171204-B |
| priorityDate |
2022-03-23-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| type |
http://data.epo.org/linked-data/def/patent/Publication |