http://rdf.ncbi.nlm.nih.gov/pubchem/patent/CN-109300111-B
Outgoing Links
| Predicate | Object |
|---|---|
| classificationCPCAdditional | http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06T2207-30024 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/G06F18-2411 http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06T7-62 http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06N3-12 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 |
| classificationIPCInventive | http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06N3-04 http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06N3-08 http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06T7-62 http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06T7-00 http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06K9-62 |
| filingDate | 2018-08-27-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| grantDate | 2020-05-12-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| publicationDate | 2020-05-12-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| publicationNumber | CN-109300111-B |
| titleOfInvention | A Chromosome Recognition Method Based on Deep Learning |
| abstract | The invention discloses a chromosome identification method based on deep learning, and belongs to the technical field of chromosome identification. At present, the method of analyzing chromosomes is basically manual operation. First of all, the testing doctor needs a lot of training time to master the knowledge of identifying each chromosome type, and the workload is heavy. Even if an experienced doctor analyzes and identifies a patient's chromosomes, the whole process generally takes more than two weeks, which is a long time period. And manual identification is highly subjective, easily affected by the external environment, and the accuracy rate is not high. The invention adopts the deep learning method to accurately and efficiently identify the chromosome type, compared with the existing identification technology, can effectively improve the analysis efficiency of the chromosome karyotype, shorten the identification and sorting time, and complete the automatic classification and sorting of the chromosome with high accuracy. At the same time, it can effectively reduce the workload of doctors without external interference, and the process is simple and reasonable, and can be widely applied to the outside world. |
| priorityDate | 2018-08-27-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| type | http://data.epo.org/linked-data/def/patent/Publication |
Incoming Links
Total number of triples: 25.