کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
10351834 | 864577 | 2005 | 22 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Improving medical diagnostic accuracy of ultrasound Doppler signals by combining neural network models
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
نرم افزارهای علوم کامپیوتر
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چکیده انگلیسی
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.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Computers in Biology and Medicine - Volume 35, Issue 6, July 2005, Pages 533-554
Journal: Computers in Biology and Medicine - Volume 35, Issue 6, July 2005, Pages 533-554
نویسندگان
Elif Derya Ãbeyli, Ä°nan Güler,