Breast　cancer　is　the　most　often　diagnosed　cancer　in　women　affecting　one　in　eight　at　the　age　of　80　in　US.　Breast　is　the　most　threatening　cancer　among　women　which　leads　to　death.　Early　diagnosis　of　breast　cancer　can　save　their　lives　which　decreases　the　mortality　rate.　Mammography　is　a　standard　screening　method　for　breast　cancer　diagnosis　that　identifies　occurrences　of　breast　cancer　in　women＇s　at　early　stages　without　symptoms.　In　this　study,　we　employed　transfer　learning　in　deep　learning　to　increase　the　neural　network＇s　performance　and　reduce　the　false　positive　rate.　In　addition,　we　proposed　a　pre-trained　VGG-19　neural　network　to　extract　features　of　individual　microcalcification　to　predict　breast　cancer.　The　proposed　method　was　evaluated　on　two　public　databases　the　CBIS-DDSM　and　DDSM　and　achieved　0.98　sensitivities　respectively.　The　proposed　method　obtained　higher　sensitivity　than　other　residual　neural　networks　and　previous　studies.　©　2023　ACM.