Background:　Screening　of　the　brain　computerised　tomography　(CT)　images　is　a　primary　method　currently　used　for　initial　detection　of　patients　with　brain　trauma　or　other　conditions.　In　recent　years,　deep　learning　technique　has　shown　remarkable　advantages　in　the　clinical　practice.　Researchers　have　attempted　to　use　deep　learning　methods　to　detect　brain　diseases　from　CT　images.　Methods　often　used　to　detect　diseases　choose　images　with　visible　lesions　from　full-slice　brain　CT　scans,　which　need　to　be　labelled　by　doctors.　This　is　an　inaccurate　method　because　doctors　detect　brain　disease　from　a　full　sequence　scan　of　CT　images　and　one　patient　may　have　multiple　concurrent　conditions　in　practice.　The　method　cannot　take　into　account　the　dependencies　between　the　slices　and　the　causal　relationships　among　various　brain　diseases.　Moreover,　labelling　images　slice　by　slice　spends　much　time　and　expense.　Detecting　multiple　diseases　from　full　slice　brain　CT　images　is,　therefore,　an　important　research　subject　with　practical　implications.　Results:　In　this　paper,　we　propose　a　model　called　the　slice　dependencies　learning　model　(SDLM).　It　learns　image　features　from　a　series　of　variable　length　brain　CT　images　and　slice　dependencies　between　different　slices　in　a　set　of　images　to　predict　abnormalities.　The　model　is　necessary　to　only　label　the　disease　reflected　in　the　full-slice　brain　scan.　We　use　the　CQ500　dataset　to　evaluate　our　proposed　model,　which　contains　1194　full　sets　of　CT　scans　from　a　total　of　491　subjects.　Each　set　of　data　from　one　subject　contains　scans　with　one　to　eight　different　slice　thicknesses　and　various　diseases　that　are　captured　in　a　range　of　30　to　396　slices　in　a　set.　The　evaluation　results　present　that　the　precision　is　67.57%,　the　recall　is　61.04%,　the　F1　score　is　0.6412,　and　the　areas　under　the　receiver　operating　characteristic　curves　(AUCs)　is　0.8934.　Conclusion:　The　proposed　model　is　a　new　architecture　that　uses　a　full-slice　brain　CT　scan　for　multi-label　classification,　unlike　the　traditional　methods　which　only　classify　the　brain　images　at　the　slice　level.　It　has　great　potential　for　application　to　multi-label　detection　problems,　especially　with　regard　to　the　brain　CT　images.　©　2020,　The　Author(s).