Microcalcification　clusters　in　mammograms　are　one　of　the　major　signs　of　breast　cancer.　However,　the　detection　of　microcalcifications　from　mammograms　is　a　challenging　task　for　radiologists　due　to　their　tiny　size　and　scattered　location　inside　a　denser　breast　composition.　Automatic　CAD　systems　need　to　predict　breast　cancer　at　the　early　stages　to　support　clinical　work.　The　intercluster　gap,　noise　between　individual　MCs,　and　individual　object＇s　location　can　affect　the　classification　performance,　which　may　reduce　the　true-positive　rate.　In　this　study,　we　propose　a　computer-vision-based　FC-DSCNN　CAD　system　for　the　detection　of　microcalcification　clusters　from　mammograms　and　classification　into　malignant　and　benign　classes.　The　computer　vision　method　automatically　controls　the　noise　and　background　color　contrast　and　directly　detects　the　MC　object　from　mammograms,　which　increases　the　classification　performance　of　the　neural　network.　The　breast　cancer　classification　framework　has　four　steps:　image　preprocessing　and　augmentation,　RGB　to　grayscale　channel　transformation,　microcalcification　region　segmentation,　and　MC　ROI　classification　using　FC-DSCNN　to　predict　malignant　and　benign　cases.　The　proposed　method　was　evaluated　on　3568　DDSM　and　2885　PINUM　mammogram　images　with　automatic　feature　extraction,　obtaining　a　score　of　0.97　with　a　2.35　and　0.99　true-positive　ratio　with　2.45　false　positives　per　image,　respectively.　Experimental　results　demonstrated　that　the　performance　of　the　proposed　method　remains　higher　than　the　traditional　and　previous　approaches.