Deep Convolutional Networks For Oct Image Classification
| but.event.date | 25.04.2019 | cs |
| but.event.title | Student EEICT 2019 | cs |
| dc.contributor.author | Hesko, Branislav | |
| dc.date.accessioned | 2020-04-16T07:19:36Z | |
| dc.date.available | 2020-04-16T07:19:36Z | |
| dc.date.issued | 2019 | cs |
| dc.description.abstract | In this work, OCT (optical coherence tomography) images are classified according to the present pathology into four distinct categories. Three different neural network models are used to classify images, each model is recent and we are achieving exceptional results on the testing dataset, which was unknown to the network during the training. Accuracy on the testing set is higher than 98% and only a few of images are classified into the wrong category. This makes our approach perspective for future automatic use. To further improve results, all three models are using transfer learning. | en |
| dc.format | text | cs |
| dc.format.extent | 437-441 | cs |
| dc.format.mimetype | application/pdf | en |
| dc.identifier.citation | Proceedings of the 25st Conference STUDENT EEICT 2019. s. 437-441. ISBN 978-80-214-5735-5 | cs |
| dc.identifier.isbn | 978-80-214-5735-5 | |
| dc.identifier.uri | http://hdl.handle.net/11012/186712 | |
| dc.language.iso | en | cs |
| dc.publisher | Vysoké učení technické v Brně, Fakulta elektrotechniky a komunikačních technologií | cs |
| dc.relation.ispartof | Proceedings of the 25st Conference STUDENT EEICT 2019 | en |
| dc.relation.uri | http://www.feec.vutbr.cz/EEICT/ | cs |
| dc.rights | © Vysoké učení technické v Brně, Fakulta elektrotechniky a komunikačních technologií | cs |
| dc.rights.access | openAccess | en |
| dc.subject | OCT | en |
| dc.subject | deep learning | en |
| dc.subject | classification | en |
| dc.subject | retina | en |
| dc.title | Deep Convolutional Networks For Oct Image Classification | en |
| dc.type.driver | conferenceObject | en |
| dc.type.status | Peer-reviewed | en |
| dc.type.version | publishedVersion | en |
| eprints.affiliatedInstitution.department | Fakulta elektrotechniky a komunikačních technologií | cs |
Files
Original bundle
1 - 1 of 1
Loading...
- Name:
- 437_eeict2019.pdf
- Size:
- 2.64 MB
- Format:
- Adobe Portable Document Format
- Description: