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Thailand has been undergoing a transition into a completely developed elderly society. The sign is becoming more apparent in the last few years, when a ratio between the number of children and mature adults has been dramatically decreasing. In additional to prevailing measures the government has to take, healthcare service for elderly people has to be readily prepared. Among diseases from which the elderly people are suffered are high blood pressure, high cholesterol and dementia. Age related dementia are gradually developed. Alzheimer's disease is however another more serious type of dementia that often drastically affect not only the patient but also their caretaker. Early diagnosis of the symptom could well enable therapeutic measures that improve their quality of life. This can be done in several ways, e.g., by medical survey and medical imaging. This paper therefore presents a robust PET image classification methods for diagnosing Alzheimer's disease in a samples drawn from Thai population. The proposed process adopted K-means clustering and Gabor Wavelet for brain segmentation and image feature extraction, respectively. To reduce the dimensions of data involved, only mean and standard deviation of pixels were extracted as features. The disease was finally classified by using a supervised machine learning in turn. Specifically, four classification methods were considered, i.e., Eigenface, Support Vector Machine, Convolutional Neural Network and proposed method. The experimental results indicated the accuracy of proposed method was up to 87%. It was appropriate to identify Alzheimer's patients from normal controls and the proposed SVM outperformed the rest.
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