Alzheimer＇s　Disease　(AD)　is　a　chronic　neurodegenerative　disease　without　effective　medications　or　supplemental　treatments.　Thus,　predicting　AD　progression　is　crucial　for　clinical　practice　and　medical　research.　Due　to　limited　neuroimaging　data,　two-dimensional　convolutional　neural　networks　(2D　CNNs)　have　been　commonly　adopted　to　differentiate　among　cognitively　normal　subjects　(CN),　people　with　mild　cognitive　impairment　(MCI),　and　AD　patients.　Therefore,　this　paper　proposes　an　ensemble　learning　(EL)　architecture　based　on　2D　CNNs,　using　a　multi　model　and　multi-slice　ensemble.　First,　the　top　11　coronal　slices　of　grey　matter　density　maps　for　AD　versus　CN　classifications　were　selected.　Second,　the　discriminator　of　a　generative　adversarial　network,　VGG16,　and　ResNet50　were　trained　with　the　selected　slices,　and　the　majority　voting　scheme　was　used　to　merge　the　multi-slice　decisions　of　each　model.　Afterwards,　those　three　classifiers　were　used　to　construct　an　ensemble　model.　Multi-slice　ensemble　learning　was　designed　to　obtain　spatial　features,　while　multi-model　integration　reduced　the　prediction　error　rate.　Finally,　transfer　learning　was　used　in　domain　adaptation　to　refine　those　CNNs,　moving　them　from　working　solely　with　AD　versus　CN　classifications　to　being　applicable　to　other　tasks.　This　ensemble　approach　achieved　accuracy　values　of　90.36%,　77.19%,　and　72.36%　when　classifying　AD　versus　CN,　AD　versus　MCI,　and　MCI　versus　CN,　respectively.　Compared　with　other　state-of-the-art　2D　studies,　the　proposed　approach　provides　an　effective,　accurate,　automatic　diagnosis　along　the　AD　continuum.　This　technique　may　enhance　AD　diagnostics　when　the　sample　size　is　limited.