Classification of Some Dementia Types Due to Feature Selection with Artificial Neural Networks

OKYAY S., ADAR N., ÖZKAN K., ŞAYLISOY S., Adapinar B. D. O., Adapinar B.

24th Signal Processing and Communication Application Conference (SIU), Zonguldak, Türkiye, 16 - 19 Mayıs 2016, ss.1121-1124 identifier identifier

  • Yayın Türü: Bildiri / Tam Metin Bildiri
  • Cilt numarası:
  • Doi Numarası: 10.1109/siu.2016.7495941
  • Basıldığı Şehir: Zonguldak
  • Basıldığı Ülke: Türkiye
  • Sayfa Sayıları: ss.1121-1124
  • Anahtar Kelimeler: Alzheimer's disease, classification, dementia, dicom, Freesurfer, magnetic resonance imaging, ALZHEIMERS-DISEASE, AUTOMATED CLASSIFICATION, PROGRESSION
  • Eskişehir Osmangazi Üniversitesi Adresli: Evet

Özet

Brains of patients with dementia show physical differences according to disease types and phases. Physical characteristics of brains such as cortical thickness and volumes of some parts have a significant effect on determining the type of the disease. Magnetic resonance imaging devices create visual files which contain patient information appropriate to the medical imaging standards. Using image processing techniques, numerical expressions of patients' brains can be extracted via these files. By means of using these numeric values with classification methods, patients can be classified. In this study, samples having three diseases: Alzheimer's disease, vascular dementia and frontotemporal dementia are used. After extracting cortical surface area, thickness and volume features, samples are classified successfully with artificial neural networks due to feature selection.