Improving medical diagnostic accuracy of ultrasound Doppler signals by combining neural network models

Ubeyli E., Guler İ.

COMPUTERS IN BIOLOGY AND MEDICINE, cilt.35, sa.6, ss.533-554, 2005 (SCI-Expanded) identifier identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 35 Sayı: 6
  • Basım Tarihi: 2005
  • Doi Numarası: 10.1016/s0010-4825(04)00047-2
  • Dergi Adı: COMPUTERS IN BIOLOGY AND MEDICINE
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.533-554
  • Anahtar Kelimeler: combined neural network model, medical diagnostic accuracy, discrete wavelet transform, Doppler signal, ophthalmic artery, internal carotid artery, PREDICTIONS, PERFORMANCE, STENOSIS, FFT, AR
  • Gazi Üniversitesi Adresli: Evet

Özet

There are a number of different quantitative models that can be used in a medical diagnostic decision support system including parametric methods (linear discriminant analysis or logistic regression), nonparametric models (k nearest neighbor or kernel density) and several neural network models. The complexity of the diagnostic task is thought to be one of the prime determinants of model selection. Unfortunately, there is no theory available to guide model selection. This paper illustrates the use of combined neural network models to guide model selection for diagnosis of ophthalmic and internal carotid arterial disorders. The ophthalmic and internal carotid arterial Doppler signals were decomposed into time-frequency representations using discrete wavelet transform and statistical features were calculated to depict their distribution. The first-level networks were implemented for the diagnosis of ophthalmic and internal carotid arterial disorders using the statistical features as inputs. To improve diagnostic accuracy, the second-level networks were trained using the outputs of the first-level networks as input data. The combined neural network models achieved accuracy rates which were higher than that of the stand-alone neural network models. (C) 2004 Elsevier Ltd. All rights reserved.