COVID-19　is　a　global　pandemic　that　has　caused　significant　global,　social,　and　economic　disruption.　To　effectively　assist　in　screening　and　monitoring　diagnosed　cases,　it　is　crucial　to　accurately　segment　lesions　from　Computer　Tomography　(CT)　scans.　Due　to　the　lack　of　labeled　data　and　the　presence　of　redundant　parameters　in　3D　CT,　there　are　still　significant　challenges　in　diagnosing　COVID-19　in　related　fields.　To　address　the　problem,　we　have　developed　a　new　model　called　the　Cascaded　3D　Dilated　convolutional　neural　network　(CD-Net)　for　directly　processing　CT　volume　data.　To　reduce　memory　consumption　when　cutting　volume　data　into　small　patches,　we　initially　design　a　cascade　architecture　in　CD-Net　to　preserve　global　information.　Then,　we　construct　a　Multi-scale　Parallel　Dilated　Convolution　(MPDC)　block　to　aggregate　features　of　different　sizes　and　simultaneously　reduce　the　parameters.　Moreover,　to　alleviate　the　shortage　of　labeled　data,　we　employ　classical　transfer　learning,　which　requires　only　a　small　amount　of　data　while　achieving　better　performance.　Experimental　results　conducted　on　the　different　public-available　datasets　verify　that　the　proposed　CD-Net　has　reduced　the　negative–positive　ratio　and　outperformed　other　existing　segmentation　methods　while　requiring　less　data.　©　2024　Elsevier　Ltd