Diagnosis of Degenerative Intervertebral Disc Disease with Deep Networks and SVM

Creative Commons License

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, (Full Text)

  • 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
  • Open Archive Collection: AVESIS Open Access Collection
  • Yıldız Technical University Affiliated: No

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.