Several　developments　in　computational　image　processing　methods　assist　the　radiologist　in　detecting　abnormal　breast　tissue　in　recent　years.　Consequently,　deep　learning-based　models　have　become　crucial　for　early　screening　and　interpretation　of　mammographic　images　for　breast　masses　diagnosis,　helping　for　successful　treatment.　Breast　masses　and　calcification　is　an　essential　parameter　for　the　prognosis　of　breast　cancer.　However,　the　mammographic　image＇s　mass　detection　needs　a　deeper　investigation　due　to　the　breast　masses＇　heterogeneity　and　anomalies＇　characteristics　that　are　easily　confused　with　other　objects　present　in　the　image.　Hence,　this　study　proposed　a　deep　learning-based　convolutional　neural　network　(ConvNet)　that　will　incorporate　both　mammography　and　clinical　variables　to　predict　and　classify　breast　masses　to　assist　the　expert＇s　decision-making　processes.　We　trained　our　proposed　model　with　322　scanned　digital　mammographic　images　of　the　MIAS　(Mammogram　Image　Analysis　Society)　dataset　and　580　images　of　the　private　dataset　to　evaluate　the　performance,　which　is　highly　imbalanced.　This　study　aimed　to　perform　an　automatic　and　comprehensive　characterization　of　breast　masses　using　appropriate　layers　deep　ConvNet　model　with　high　accuracy　true-positive　rate,　decreased　error　rate　and　applying　data-augmentation　techniques.　We　obtained　a　classification　accuracy　of　97%　applying　the　filtered　deep　features,　which　is　the　best　performance　from　the　existing　approaches.