Pneumonia　is　an　infectious　disease　that　endangers　human　health.　With　advancements　in　science　and　technology,　deep　learning-driven　techniques　have　gained　prominence　in　this　field.　However,　their　applicability　to　clinical　practice　remains　limited　because　they　mostly　neglect　three　key　points:　focus　on　local　lesion　regions,　multi-level　feature　fusion,　and　sequential　collaborative　decision-making.　In　this　paper,　we　present　a　novel　multi-level　ensemble　network　for　automatic　pneumonia　diagnosis　from　full　slice　CT　images,　inspired　by　the　“Focus,　Fusion,　Collaboration”　strategy.　Our　proposed　model　involves　three　modules:　the　global–local　feature　extraction　module　is　first　designed　to　fully　extract　the　global　structure　information　and　local　lesion　details;　subsequently,　the　multi-level　feature　fusion　module　is　responsible　for　integrating　the　above-mentioned　global　and　local　information;　finally,　the　sequential　pneumonia　prediction　module　is　utilized　to　learn　the　contextual　relationship　between　the　adjacent　slices,　thus　generating　the　final　diagnosis　results.　Building　upon　mimicking　the　diagnostic　behavior　from　real-world　clinical　scenarios,　our　model　enables　the　integration　of　multiple　types　of　information　(including　global　structure　information,　local　lesion　features,　and　slice　dependencies)　and　sequential　pneumonia　diagnosis.　Extensive　comparative　experiments　are　conducted　to　verify　the　feasibility　and　effectiveness　of　our　proposed　method.　The　experimental　results　show　that　our　model　can　obtain　an　accuracy　of　91.4%　in　a　four-class　pneumonia　diagnosis　task,　outperforming　the　other　classical　works.　©　2025　Elsevier　Ltd