Emphysema Discrimination from Raw HRCT Images by Convolutional Neural Networks


Karabulut E. M. , İBRİKÇİ T.

9th International Conference on Electrical and Electronics Engineering (ELECO), Bursa, Türkiye, 26 - 28 Kasım 2015, ss.705-708 identifier

  • Cilt numarası:
  • Basıldığı Şehir: Bursa
  • Basıldığı Ülke: Türkiye
  • Sayfa Sayıları: ss.705-708

Özet

Emphysema is a chronic lung disease that causes breathlessness. HRCT is the reliable way of visual demonstration of emphysema in patients. The fact that dangerous and widespread nature of the disease require immediate attention of a doctor with a good degree of specialized anatomical knowledge. This necessitates the development of computer-based automatic identification system. This study aims to investigate the deep learning solution for discriminating emphysema subtypes by using raw pixels of input HRCT images of lung. Convolutional Neural Network (CNN) is used as the deep learning method for experiments carried out in the Caffe deep learning framework. As a result, promising percentage of accuracy is obtained besides low processing time.