Diagnosis And Modelling Of Alzheimer's Disease Through Neural Network Analyses Of Pet Studies
| Autor: | J.S. Kippenhan, J.H. Nagel |
|---|---|
| Rok vydání: | 2005 |
| Předmět: |
medicine.diagnostic_test
Positronen emissions tomographie Artificial neural network Alzheimer krankheit business.industry Template matching Disease Machine learning computer.software_genre Positron emission tomography medicine Cerebral function Signal processing algorithms Artificial intelligence business Psychology computer |
| Zdroj: | [1990] Proceedings of the Twelfth Annual International Conference of the IEEE Engineering in Medicine and Biology Society. |
| DOI: | 10.1109/iembs.1990.691833 |
| Popis: | The back-propagation neural network algorithm was applied to the analysis of regional patterns in cerebral function, a~ demonstrated in positron emission tomography (PET). A trained network was able to successfully distinguish PET scans of normal subjects from PET scans of Alzheimer's Disease patients. It is concluded that the combination of PET and neural networks is a useful diagnostic tool for Alzheimer's Disease. A new paradigm for back-propagation learning is discussed which emphasizes its similarity to template matching. It is demonstrated that, under certain circumstances, the back-propagation network can be used as an estimation tool, as weB as a classification tool, i.e., a trained neural network can indicate the criteria by which its classifications are performed. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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