Breast　tumor　(BT)　is　the　second　most　common　health　problem　for　women.　Traditional　diagnosis　methods　can　be　very　labor-intensive　and　time-consuming　with　the　risk　of　making　a　wrong　diagnosis.　Computer　vision　and　imaging　processing　techniques　using　machine　learning　(ML)　methods　are　emerging　to　aide　in　clinical　diagnosis.　Some　machine　learning　methods　have　yielded　an　accuracy　of　85%　using　a　single-layer　classifier.　In　this　study　Inception-V3,　a　two-layer　classifier　of　transfer　machine　learning　tool　was　used　for　image　processing　with　enhancement　technologies　and　for　the　classification　of　breast　tumor　histopathological　types.　Results　showed　that　image　augmentation　with　dual-layer　transfer　machine　learning　algorithms　yielded　an　accuracy　of　95.6%　in　identification　of　breast　tumor　pathologic　types,　which　was　higher　than　previously　reported　methods　in　the　literature.　Different　image　preprocessing　methods,　dataset　preparing　methods,　and　classifier　architectures　were　also　studied　to　identify　the　optimal　algorithm.　Results　showed　that　multiple-layer　processing　algorithms　using　color　images,　instead　of　black　and　white　images,　yielded　a　better　accuracy　in　histopathological　type　classification.　©　2019　Association　for　Computing　Machinery.