Depression　is　a　common　mental　illness　characterized　by　symptoms　such　as　low　mood,　pessimism,　and　insomnia.　In　this　study,　we　developed　a　deep　Dual-Stream　CNN　to　automatically　diagnose　and　classify　depression　in　expression　video　sequences.　The　network　has　two　branches　that　extract　static　features　and　dynamic　features　from　static　and　dynamic　expressions,　respectively,　which　are　then　fused　for　depression　classification.　The　experiments　were　performed　on　the　AVEC2014　database,　and　the　results　showed　that　the　Dual-Stream　model　significantly　improved　the　classification　performance　of　depression,　achieving　an　accuracy　of　69.08%　in　depression　categorization.　©　2023　SPIE.