Automatic　diagnosis of　Alzheimer’s　disease　(AD)　and　mild　cognitive　impairment　(MCI)　from　3D　brain　magnetic　resonance　(MR)　images　play　an　important　role　in　the　early　treatment　of　dementia　disease.　Deep　learning　architectures can　extract　potential　features　of　dementia　disease　and　capture　brain　anatomical　changes　from　MRI　scans.　Given　the　high　dimension　and　complex　features　of　the　3D　medical　images,　computer-aided　diagnosis is　still　confronted　with　challenges.　Firstly,　compared　with　the　number　of　learnable　parameters,　the　number　of　training　samples　is　very　limited,　which　can　cause　overfitting　problems.　Secondly,　the　deepening of　the　network　layer　makes　gradient　information　gradually　weaken　and　even　disappears　in　the　process　of　transmission,　resulting　in　mode　collapse.　This　chapter　proposed　an　ensemble　of　3D　densely connected　convolutional　networks　for　AD　and　MCI　diagnosis　from　3D　MRIs.　Dense　connections　were　introduced　to　maximize　the　information　flow,　where　each　layer　connects　with　all　subsequent　layers　directly.　Bottleneck　layers　and　transition　layers　are　also　employed　to　reduce　parameters　and　lead　to　more　compact　models.　Then　the　probability-based　fusion　method　was　employed　to　combine　3D-DenseNets　with　different　architectures.　Extensive　experiments　were　conducted　to　analyze　the　performance　of　3D-DenseNet　with　different　hyperparameters　and　architectures.　Superior　performance　of　the　proposed　model　was　demonstrated　on　ADNI　dataset.　©　2020　Springer　Nature　Singapore　Pte　Ltd　2020.