Diagnosis of Degenerative Intervertebral Disc Disease with Deep Networks and SVM


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Oktay A. B. , Akgul Y. S.

31st International Symposium on Computer and Information Sciences (ISCIS), Krakow, Poland, 27 - 28 October 2016, vol.659, pp.253-261 identifier identifier

  • Publication Type: Conference Paper / Full Text
  • Volume: 659
  • Doi Number: 10.1007/978-3-319-47217-1_27
  • City: Krakow
  • Country: Poland
  • Page Numbers: pp.253-261
  • Keywords: Degenerative disc disease, Auto encoders, Deep network

Abstract

Computer aided diagnosis of degenerative intervertebral disc disease is a challenging task which has been targeted many times by computer vision and image processing community. This paper proposes a deep network approach for the diagnosis of degenerative intervertebral disc disease. Different from the classical deep networks, our system uses non-linear filters between the network layers that introduce domain dependent information into the network training for a faster training with lesser amount of data. The proposed system takes advantage of the unsupervised feature extraction with deep networks while requiring only a small amount of training data, which is a major problem for medical image analysis where obtaining large amounts of patient data is very difficult. The method is validated on a dataset containing 102 lumbar MR images. State-of-the-art hand-crafted feature extraction algorithms are compared with the unsupervisedly learned features and the proposed method outperforms the hand-crafted features.