Liver　segmentation　and　recognition　from　computed　tomography　(CT)　images　is　a　warm　topic　in　image　processing　which　is　helpful　for　doctors　and　practitioners.　Currently,　many　deep　learning　methods　are　used　for　liver　segmentation　that　takes　a　long　time　to　train　the　model　which　makes　this　task　challenging　and　limited　to　larger　hardware　resources.　In　this　research,　we　proposed　a　very　lightweight　convolutional　neural　network　(CNN)　to　extract　the　liver　region　from　CT　scan　images.　The　suggested　CNN　algorithm　consists　of　3　convolutional　and　2　fully　connected　layers,　where　softmax　is　used　to　discriminate　the　liver　from　background.　Random　Gaussian　distribution　is　used　for　weight　initialization　which　achieved　a　distance-preserving-embedding　of　the　information.　The　proposed　network　is　known　as　Ga-CNN　(Gaussian-weight　initialization　of　CNN).　General　experiments　are　performed　on　three　benchmark　datasets　including　MICCAI　SLiver＇07,　3Dircadb01,　and　LiTS17.　Experimental　results　show　that　the　proposed　method　performed　well　on　each　benchmark　dataset.