Gaussian　smoothing　(GS)　is　a　spatial　low　pass　filtering　method　widely　used　in　neuroimaging　preprocessing.　Full　width　at　half　maximum　(FWHM)　is　a　common　parameter　when　the　imaging　data　convolved　with　GS　kernel.　The　convolutional　neural　networks　(CNNs)　can　be　considered　as　the　feature　extractor,　which　is　implemented　by　applying　a　series　of　different　filters.　However,　the　influence　of　kernel　size　of　GS　for　feature　extraction　remains　unclear.　In　this　study,　we　describe　an　automatic　AD　classification　algorithm　that　is　built　on　a　pre-Trained　CNN　model,　AlexNet　for　feature　extraction　and　support　vector　machine　(SVM)　for　classification.　The　algorithm　was　trained　and　tested　using　the　structural　Magnetic　Resonance　Imaging　(sMRI)　data　from　Alzheimer＇s　Disease　Neuroimaging　Initiative　(ADNI).　The　data　used　in　this　study　include　191　Alzheimer＇s　disease　(AD)　patients　and　103　normal　control　(NC)　subjects.　We　evaluate　the　influence　of　FWHM　on　classification　performance.　When　FWHM　is　0mm,　the　classification　accuracy　obtained　the　highest　value　for　AD　and　NC,　which　reached　91.5%,　92.4%,　89.0%　for　conv3,　conv4　and　conv5　of　AlexNet　respectively.　The　classification　accuracy　at　each　layer　is　relatively　low　when　FWHM　is　8mm.　The　result　suggests　that　the　higher　smooth　value　may　have　a　negative　effect　on　feature　extraction　of　CNNs　during　AD　classification.　©　2019　Association　for　Computing　Machinery.