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Presented is a Gabor wavelet (GW) based analysis of functional brain images by integrating the 2D GW representation of the images for image classification applied to early diagnosis of Alzheimer's disease. The 2D GW representation of the brain images is processed by means of a principal component analysis (PCA) for feature extraction and support vector machines (SVMs) for image classification. The proposed method yields up to 96% classification accuracy with 100% sensitivity, thus becoming an accurate method for image classification. Comparison between the conventional PCA plus SVM method and the proposed method is also provided. In addition, the proposed method with Gabor wavelets increases the outcomes of other methods based on voxel as features (VAF), PCA, and so on.