One　of　the　most　common　methods　for　diagnosing　coronary　artery　disease　is　the　use　of　the　coronary　artery　calcium　score　CT.　However,　the　current　diagnostic　method　using　the　coronary　artery　calcium　score　CT　requires　a　considerable　time,　because　the　radiologist　must　manually　check　the　CT　images　one-by-one,　and　check　the　exact　range.　In　this　paper,　three　CNN　models　are　applied　for　1200　normal　cardiovascular　CT　images,　and　1200　CT　images　in　which　calcium　is　present　in　the　cardiovascular　system.　We　conduct　the　experimental　test　by　classifying　the　CT　image　data　into　the　original　coronary　artery　calcium　score　CT　images　containing　the　entire　rib　cage,　the　cardiac　segmented　images　that　cut　out　only　the　heart　region,　and　cardiac　cropped　images　that　are　created　by　using　the　cardiac　images　that　are　segmented　into　nine　sub-parts　and　enlarged.　As　a　result　of　the　experimental　test　to　determine　the　presence　of　calcium　in　a　given　CT　image　using　Inception　Resnet　v2,　VGG,　and　Resnet　50　models,　the　highest　accuracy　of　98.52%　was　obtained　when　cardiac　cropped　image　data　was　applied　using　the　Resnet　50　model.　Therefore,　in　this　paper,　it　is　expected　that　through　further　research,　both　the　simple　presence　of　calcium　and　the　automation　of　the　calcium　analysis　score　for　each　coronary　artery　calcium　score　CT　will　become　possible.