Mild　cognitive　impairment　(MCI)　detection　using　magnetic　resonance　image　(MRI),　plays　a　crucial　role　in　the　treatment　of　dementia　disease　at　an　early　stage.　Deep　learning　architecture　produces　impressive　results　in　such　research.　Algorithms　require　a　large　number　of　annotated　datasets　for　training　the　model.　In　this　study,　we　overcome　this　issue　by　using　layer-wise　transfer　learning　as　well　as　tissue　segmentation　of　brain　images　to　diagnose　the　early　stage　of　Alzheimer＇s　disease　(AD).　In　layer-wise　transfer　learning,　we　used　the　VGG　architecture　family　with　pre-trained　weights.　The　proposed　model　segregates　between　normal　control　(NC),　the　early　mild　cognitive　impairment　(EMCI),　the　late　mild　cognitive　impairment　(LMCI),　and　the　AD.　In　this　paper,　85　NC　patients,　70　EMCI,　70　LMCI,　and　75　AD　patients　access　form　the　Alzheimer＇s　Disease　Neuroimaging　Initiative　(ADNI)　database.　Tissue　segmentation　was　applied　on　each　subject　to　extract　the　gray　matter　(GM)　tissue.　In　order　to　check　the　validity,　the　proposed　method　is　tested　on　preprocessing　data　and　achieved　the　highest　rates　of　the　classification　accuracy　on　AD　vs　NC　is　98.73%,　also　distinguish　between　EMCI　vs　LMCI　patients　testing　accuracy　83.72%,　whereas　remaining　classes　accuracy　is　more　than　80%.　Finally,　we　provide　a　comparative　analysis　with　other　studies　which　shows　that　the　proposed　model　outperformed　the　state-of-the-art　models　in　terms　of　testing　accuracy.