The　integration　of　deep　learning　(DL)　and　digital　breast　tomosynthesis　(DBT)　presents　a　unique　opportunity　to　improve　the　reliability　of　breast　cancer　(BC)　detection　and　diagnosis　while　accommodating　novel　imaging　techniques.　This　study　utilizes　the　publicly　available　Mammographic　Image　Analysis　Society　(MIAS)　database　v1.21　to　evaluate　DL　algorithms　in　identifying　and　categorizing　cancerous　tissue.　The　dataset　has　undergone　preprocessing　and　has　been　confirmed　to　be　of　exceptional　quality.　Transfer　learning　techniques　are　employed　with　three　pre-trained　models　-　MobileNet,　Xception,　DenseNet,　and　MobileNet　LSTM　-　to　improve　performance　on　the　target　task.　Stacking　ensemble　learning　techniques　will　be　utilized　to　combine　the　predictions　of　the　best-performing　models　to　make　the　final　prediction　for　the　presence　of　BC.　The　evaluation　will　measure　the　performance　of　each　model　using　standard　evaluation　metrics,　including　accuracy　(ACC),　precision　(PREC),　recall　(REC),　and　F1-score　(F1-S).　This　study　highlights　the　potential　of　DL　in　enhancing　diagnostic　imaging　and　advancing　healthcare.　©　The　Author(s),　under　exclusive　license　to　Springer　Nature　Singapore　Pte　Ltd　2024.