Brain　tumors　have　become　a　leading　cause　of　death　around　the　globe.　The　main　reason　for　this　epidemic　is　the　difficulty　conducting　a　timely　diagnosis　of　the　tumor.　Fortunately,　magnetic　resonance　images　(MRI)　are　utilized　to　diagnose　tumors　in　most　cases.　The　performance　of　a　Convolutional　Neural　Network　(CNN)　depends　on　many　factors　(i.e.,　weight　initialization,　optimization,　batches　and　epochs,　learning　rate,　activation　function,　loss　function,　and　network　topology),　data　quality,　and　specific　combinations　of　these　model　attributes.　When　we　deal　with　a　segmentation　or　classification　problem,　utilizing　a　single　optimizer　is　considered　weak　testing　or　validity　unless　the　decision　of　the　selection　of　an　optimizer　is　backed　up　by　a　strong　argument.　Therefore,　optimizer　selection　processes　are　considered　important　to　validate　the　usage　of　a　single　optimizer　in　order　to　attain　these　decision　problems.　In　this　paper,　we　provides　a　comprehensive　comparative　analysis　of　popular　optimizers　of　CNN　to　benchmark　the　segmentation　for　improvement.　In　detail,　we　perform　a　comparative　analysis　of　10　different　state-of-the-art　gradient　descent-based　optimizers,　namely　Adaptive　Gradient　(Adagrad),　Adaptive　Delta　(AdaDelta),　Stochastic　Gradient　Descent　(SGD),　Adaptive　Momentum　(Adam),　Cyclic　Learning　Rate　(CLR),　Adaptive　Max　Pooling　(Adamax),　Root　Mean　Square　Propagation　(RMS　Prop),　Nesterov　Adaptive　Momentum　(Nadam),　and　Nesterov　accelerated　gradient　(NAG)　for　CNN.　The　experiments　were　performed　on　the　BraTS2015　data　set.　The　Adam　optimizer　had　the　best　accuracy　of　99.2%　in　enhancing　the　CNN　ability　in　classification　and　segmentation.