Alzheimer's disease (AD) is a progressive brain disease that causes a different pattern of brain atrophy from normal aging. Early identification of AD is crucial since the progression of the disease can be slowed down by medication. In the field of image recognition, its accuracy has been significantly improved by using convolutional neural networks (CNNs). Similarly, in the field of medical image processing, researches on the diagnostic support using CNN have been studied. In this paper, we propose an AD classification method using CNN, inspired by the success of CNNs in brain age estimation. Through experiments using a large-scale database, we demonstrate the effectiveness of our proposed method.