Mild　cognitive　impairment　(MCI)　is　the　prodromal　stage　of　Alzheimer＇s　disease　(AD).　Identifying　MCI　subjects　who　are　at　high　risk　of　converting　to　AD　is　crucial　for　effective　treatments.　In　this　study,　a　deep　learning　approach　based　on　convolutional　neural　networks　(CNN),　is　designed　to　accurately　predict　MCI-to-AD　conversion　with　magnetic　resonance　imaging　(MRI)　data.　First,　MRI　images　are　prepared　with　age-correction　and　other　processing.　Second,　local　patches,　which　are　assembled　into　2.5　dimensions,　are　extracted　from　these　images.　Then,　the　patches　from　AD　and　normal　controls　(NC)　are　used　to　train　a　CNN　to　identify　deep　learning　features　of　MCI　subjects.　After　that,　structural　brain　image　features　are　mined　with　FreeSurfer　to　assist　CNN.　Finally,　both　types　of　features　are　fed　into　an　extreme　learning　machine　classifier　to　predict　the　AD　conversion.　The　proposed　approach　is　validated　on　the　standardized　MRI　datasets　from　the　Alzheimer＇s　Disease　Neuroimaging　Initiative　(ADNI)　project.　This　approach　achieves　an　accuracy　of　79.9%　and　an　area　under　the　receiver　operating　characteristic　curve　(AUC)　of　86.1%　in　leave-one-out　cross　validations.　Compared　with　other　state-of-the-art　methods,　the　proposed　one　outperforms　others　with　higher　accuracy　and　AUC,　while　keeping　a　good　balance　between　the　sensitivity　and　specificity.　Results　demonstrate　great　potentials　of　the　proposed　CNN-based　approach　for　the　prediction　of　MCI-to-AD　conversion　with　solely　MRI　data.　Age　correction　and　assisted　structural　brain　image　features　can　boost　the　prediction　performance　of　CNN.