Detail publikace

Deep convolutional networks for OCT image classification

HESKO, B.

Originální název

Deep convolutional networks for OCT image classification

Typ

článek ve sborníku ve WoS nebo Scopus

Jazyk

angličtina

Originální abstrakt

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.

Klíčová slova

OCT, deep learning, classification, retina

Autoři

HESKO, B.

Vydáno

25. 4. 2019

Nakladatel

Vysoké učení technické vBrně, Fakulta elektrotechniky a komunikačních technologií

Místo

Brno

ISBN

978-80-214-5735-5

Kniha

Proceedings of the 25th Conference STUDENT EEICT 2019

Strany od

437

Strany do

442

Strany počet

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"
}