Patients　with　breast　cancer　are　prone　to　serious　health-related　complications　with　higher　mortality.　The　primary　reason　might　be　a　misinterpretation　of　radiologists　in　recognizing　suspicious　lesions　due　to　technical　issues　in　imaging　qualities　and　heterogeneous　breast　densities　which　increases　the　false-(positive　and　negative)　ratio.　Early　intervention　is　significant　in　establishing　an　up-to-date　prognosis　process　which　can　successfully　mitigate　complications　of　disease　with　higher　recovery.　The　manual　screening　of　breast　abnormalities　through　traditional　machine　learning　schemes　misinterpret　the　inconsistent　feature-extraction　process　which　poses　a　problem,　i.e.,　patients　being　called-back　for　biopsies　to　eliminates　the　suspicions.　However,　several　deep　learning-based　methods　have　been　developed　for　reliable　breast　cancer　prognosis　and　classification　but　very　few　of　them　provided　a　comprehensive　overview　of　lesions　segmentation.　This　research　focusses　on　providing　benefits　and　risks　of　breast　multi-imaging　modalities,　segmentation　schemes,　feature　extraction,　classification　of　breast　abnormalities　through　state-of-the-art　deep　learning　approaches.　This　research　also　explores　various　well-known　databases　using　＂Breast　Cancer＂　keyword　to　present　a　comprehensive　survey　on　existing　diagnostic　schemes　to　open-up　new　research　challenges　for　radiologists　and　researchers　to　intervene　as　early　as　possible　to　develop　an　efficient　and　reliable　breast　cancer　prognosis　system　using　prominent　deep　learning　schemes.