Brain　tumors　affect　the　normal　functioning　of　the　brain　and　if　not　treated　in　time　these　cancerous　cells　may　affect　the　other　tissues,　blood　vessels,　and　nerves　surrounding　these　cells.　Today,　a　large　population　worldwide　is　affected　by　the　precarious　disease　of　the　brain　tumor.　Healthy　tissues　of　the　brain　are　suspected　to　be　damaged　because　of　tumors　that　become　the　most　significant　reason　for　a　large　number　of　deaths　nowadays.　Therefore,　their　early　detection　is　necessary　to　prevent　patients　from　unfortunate　mishaps　resulting　in　loss　of　lives.　The　manual　detection　of　brain　tumors　is　a　challenging　task　due　to　discrepancies　in　appearance　in　terms　of　shape,　size,　nucleus,　etc.　As　a　result,　an　automatic　system　is　required　for　the　early　detection　of　brain　tumors.　In　this　paper,　the　detection　of　tumors　in　brain　cells　is　carried　out　using　a　deep　convolutional　neural　network　with　stochastic　gradient　descent　(SGD)　optimization　algorithm.　The　multi-classification　of　brain　tumors　is　performed　using　the　ResNet-50　model　and　evaluated　on　the　public　Kaggle　brain-tumor　dataset.　The　method　achieved　99.82%　and　99.5%　training　and　testing　accuracy,　respectively.　The　experimental　result　indicates　that　the　proposed　model　outperformed　baseline　methods,　and　provides　a　compelling　reason　to　be　applied　to　other　diseases.