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, Polonya, 27 - 28 Ekim 2016, cilt.659, ss.253-261 identifier identifier

  • Yayın Türü: Bildiri / Tam Metin Bildiri
  • Cilt numarası: 659
  • Doi Numarası: 10.1007/978-3-319-47217-1_27
  • Basıldığı Şehir: Krakow
  • Basıldığı Ülke: Polonya
  • Sayfa Sayıları: ss.253-261
  • Anahtar Kelimeler: Degenerative disc disease, Auto encoders, Deep network
  • Yıldız Teknik Üniversitesi Adresli: Hayır

Özet

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.