Alzheimer’s disease is one of the most common diseases in the 21st century. Alzheimer's patients lose their brain cells gradually and eventually die. It is often diagnosed when the symptoms appear and little work can be done for the patient. Using of learning algorithms is useful for diagnosing of Alzheimer. Previous studies used Support Vector Machine, K-Nearest Neighbor, and Linear Discriminant Analysis in order to diagnose the disease. These methods have some problems such as low accuracy, high computation complexity or high execute time. Therefore in this research, a method based on brain emotional learning and wavelet feature is used. First, the white and gray matters of the brain were separated by a threshold selection method. Second, the texture properties of the images were extracted by wavelet transform algorithm. Third, the dimensional reduction is done on the properties extracted by principal component analysis. Finally, the features were classified using Brain Emotional Learning Algorithm and Brain Emotional Learning Based Pattern Recognizer. Results showed that run time of brain emotional learning algorithm is 0.22 second and Brain Emotional Learning algorithm with 95% accuracy and Brain Emotional Learning Based Pattern Recognizer with 97% accuracy are better than Support Vector Machine with 83% accuracy.
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