Early detection of alzheimer’s disease with convolutional neural network
International Clinical Neuroscience Journal,
Vol. 9 (2022),
The main purpose of this study is to provide a method for early diagnosis of Alzheimer's disease. This disease reduces memory function by destroying neurons in the nervous system and reducing connections and neural interactions. Alzheimer's disease is on the rise and there is no cure for it. With the help of medical image processing, Alzheimer's disease is determined and the similarity of the characteristics of brain signals with medical images is determined. Then, by presenting the characteristics of effective brain signals, the mild Alzheimer's group is determined. The level of this disease should be diagnosed according to the relationship between this disease and different features in the brain signal and medical images. First, with appropriate preprocessing, nonlinear properties such as phase diagram, correlation dimension, entropy and Lyapunov exponential are extracted and classification is done using convolutional neural network. The use of deep learning methods, including channel neural network, can have more appropriate and accurate results among other classification methods. The accuracy of the results in the reminder period is 97.5% for the brain signal and 99% for the MRI images, which is an acceptable result.
- Alzheimer's disease, EEG brain signal, MRI images, entropy, Lyapunov exponential , correlation dimension, convolutional neural network.
How to Cite
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