HEp-2 Cell Classification using a Deep Neural Network Trained for Natural Image Classification


BENLİGİRAY B., ÇINAR AKAKIN H.

24th Signal Processing and Communication Application Conference (SIU), Zonguldak, Turkey, 16 - 19 May 2016, pp.1361-1364 identifier identifier

  • Publication Type: Conference Paper / Full Text
  • Doi Number: 10.1109/siu.2016.7496001
  • City: Zonguldak
  • Country: Turkey
  • Page Numbers: pp.1361-1364
  • Keywords: deep convolutional neural networks, image classification, HEp-2 cells, indirect immunofluorescence
  • Anadolu University Affiliated: Yes

Abstract

Deep convolutional neural networks is a recently developed method that yields very successful results in image classification. Deep neural networks, which have a high number of parameters, require a large amount of data to avoid overfitting during training. For applications in which the available data is not adequate to train a deep neural network from the scratch, deep neural networks trained for similar objectives can be used as a starting point. In this study, cell images are classified using a deep neural network trained to classify objects in natural images. Even though classification of natural images and cell images are very different objectives, cell images are able to be classified with 74.1% mean class accuracy. The results show that features used for visual classification by deep convolutional neural networks may be more universal than assumed.