Simple　Summary　Diagnosing　breast　cancer　masses　and　calcification　clusters　is　crucial　in　mammography,　which　reduces　disease　consequences　and　initiates　treatment　at　an　early　stage.　A　misinterpretation　of　mammography　may　lead　to　an　unneeded　biopsy　of　the　false-positive　results,　decreasing　the　patient＇s　chances　of　survival.　This　study　aims　to　increase　the　probability　of　early　breast　mass　identification　to　ensure　better　treatment　and　minimize　mortality　risk.　However,　this　study　proposes　a　deep　learning　method　based　on　convolutional　neural　networks　to　extract　features　of　varying　densities　and　classify　normal　and　suspicious　mammography　regions.　Two　different　experiments　were　carried　out　to　validate　the　consistency　of　diagnoses　and　classification.　The　first　experiment　consisted　of　five　end-to-end　pre-trained　and　fine-tuned　deep　convolution　neural　networks.　Additionally,　the　deep　features　extracted　are　used　to　train　the　support　vector　machine　algorithm,　resulting　in　an　outstanding　performance　in　the　second　experiment.　Furthermore,　this　study　confirms　an　improvement　in　mass　recognition　accuracy　through　data　cleaning,　preprocessing,　and　augmentation.　Our　deep　learning　hybrid　model　obtained　a　classification　accuracy　of　97.8%,　outperforming　the　current　state-of-the-art　approaches.　The　proposed　model＇s　improvements　are　appropriated　in　conventional　pathological　practices　that　conceivably　reduce　the　pathologist＇s　strain　in　predicting　clinical　outcomes　by　analyzing　patients＇　mammography　images.　Background:　Diagnosing　breast　cancer　masses　and　calcification　clusters　have　paramount　significance　in　mammography,　which　aids　in　mitigating　the　disease＇s　complexities　and　curing　it　at　early　stages.　However,　a　wrong　mammogram　interpretation　may　lead　to　an　unnecessary　biopsy　of　the　false-positive　findings,　which　reduces　the　patient＇s　survival　chances.　Consequently,　approaches　that　learn　to　discern　breast　masses　can　reduce　the　number　of　misconceptions　and　incorrect　diagnoses.　Conventionally　used　classification　models　focus　on　feature　extraction　techniques　specific　to　a　particular　problem　based　on　domain　information.　Deep　learning　strategies　are　becoming　promising　alternatives　to　solve　the　many　challenges　of　feature-based　approaches.　Methods:　This　study　introduces　a　convolutional　neural　network　(ConvNet)-based　deep　learning　method　to　extract　features　at　varying　densities　and　discern　mammography＇s　normal　and　suspected　regions.　Two　different　experiments　were　carried　out　to　make　an　accurate　diagnosis　and　classification.　The　first　experiment　consisted　of　five　end-to-end　pre-trained　and　fine-tuned　deep　convolution　neural　networks　(DCNN).　The　in-depth　features　extracted　from　the　ConvNet　are　also　used　to　train　the　support　vector　machine　algorithm　to　achieve　excellent　performance　in　the　second　experiment.　Additionally,　DCNN　is　the　most　frequently　used　image　interpretation　and　classification　method,　including　VGGNet,　GoogLeNet,　MobileNet,　ResNet,　and　DenseNet.　Moreover,　this　study　pertains　to　data　cleaning,　preprocessing,　and　data　augmentation,　and　improving　mass　recognition　accuracy.　The　efficacy　of　all　models　is　evaluated　by　training　and　testing　three　mammography　datasets　and　has　exhibited　remarkable　results.　Results:　Our　deep　learning　ConvNet+SVM　model　obtained　a　discriminative　training　accuracy　of　97.7%　and　validating　accuracy　of　97.8%,　contrary　to　this,　VGGNet16　method　yielded　90.2%,　93.5%　for　VGGNet19,　63.4%　for　GoogLeNet,　82.9%　for　MobileNetV2,　75.1%　for　ResNet50,　and　72.9%　for　DenseNet121.　Conclusions:　The　proposed　model＇s　improvement　and　validation　are　appropriated　in　conventional　pathological　practices　that　conceivably　reduce　the　pathologist＇s　strain　in　predicting　clinical　outcomes　by　analyzing　patients＇　mammography　images.