Machine　and　deep　learning　algorithms　are　rapidly　growing　in　dynamic　research　of　medical　imaging.　Currently,　substantial　efforts　are　developed　for　the　enrichment　of　medical　imaging　applications　using　these　algorithms　to　diagnose　the　errors　in　disease　diagnostic　systems　which　may　result　in　extremely　ambiguous　medical　treatments.　Machine　and　deep　learning　algorithms　are　important　ways　in　medical　imaging　to　predict　the　symptoms　of　early　disease.　Deep　learning　techniques,　in　specific　convolutional　networks,　have　promptly　developed　a　methodology　of　special　for　investigating　medical　images.　It　uses　the　supervised　or　unsupervised　algorithms　using　some　specific　standard　dataset　to　indicate　the　predictions.　We　survey　image　classification,　object　detection,　pattern　recognition,　reasoning　etc.　concepts　in　medical　imaging.　These　are　used　to　improve　the　accuracy　by　extracting　the　meaningful　patterns　for　the　specific　disease　in　medical　imaging.　These　ways　also　indorse　the　decision-making　procedure.　The　major　aim　of　this　survey　is　to　highlight　the　machine　learning　and　deep　learning　techniques　used　in　medical　images.　We　intended　to　provide　an　outline　for　researchers　to　know　the　existing　techniques　carried　out　for　medical　imaging,　highlight　the　advantages　and　drawbacks　of　these　algorithms,　and　to　discuss　the　future　directions.　For　the　study　of　multi-dimensional　medical　data,　machine　and　deep　learning　provide　a　commendable　technique　for　creation　of　classification　and　automatic　decision　making.　This　paper　provides　a　survey　of　medical　imaging　in　the　machine　and　deep　learning　methods　to　analyze　distinctive　diseases.　It　carries　consideration　concerning　the　suite　of　these　algorithms　which　can　be　used　for　the　investigation　of　diseases　and　automatic　decision-making.　©　2019　IEEE.