Publication detail

Deep convolutional networks for OCT image classification

HESKO, B.

Original Title

Deep convolutional networks for OCT image classification

Type

conference paper

Language

English

Original 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.

Keywords

OCT, deep learning, classification, retina

Authors

HESKO, B.

Released

25. 4. 2019

Publisher

Vysoké učení technické vBrně, Fakulta elektrotechniky a komunikačních technologií

Location

Brno

ISBN

978-80-214-5735-5

Book

Proceedings of the 25th Conference STUDENT EEICT 2019

Pages from

437

Pages to

442

Pages count

5

BibTex

@inproceedings{BUT156730,
  author="Branislav {Hesko}",
  title="Deep convolutional networks for OCT image classification",
  booktitle="Proceedings of the 25th Conference STUDENT EEICT 2019",
  year="2019",
  pages="437--442",
  publisher="Vysoké učení technické vBrně, Fakulta elektrotechniky a komunikačních technologií",
  address="Brno",
  isbn="978-80-214-5735-5"
}