The　present　study　aimed　to　evaluate　the　performance　of　convolutional　neural　networks　(CNNs)　that　were　trained　with　small　datasets　using　different　strategies　in　the　detection　of　proximal　caries　at　different　levels　of　severity　on　periapical　radiographs.　Small　datasets　containing　800　periapical　radiographs　were　randomly　categorized　into　a　training　and　validation　dataset　(n　=　600)　and　a　test　dataset　(n　=　200).　A　pretrained　Cifar-10Net　CNN　was　used　in　the　present　study.　Different　training　strategies　were　used　to　train　the　CNN　model　independently;　these　strategies　were　defined　as　image　recognition　(IR),　edge　extraction　(EE),　and　image　segmentation　(IS).　Different　metrics,　such　as　sensitivity　and　area　under　the　receiver　operating　characteristic　curve　(AUC),　for　the　trained　CNN　and　human　observers　were　analysed　to　evaluate　the　performance　in　detecting　proximal　caries.　IR,　EE,　and　IS　recognition　modes　and　human　eyes　achieved　AUCs　of　0.805,　0.860,　0.549,　and　0.767,　respectively,　with　the　EE　recognition　mode　having　the　highest　values　(p　all　＜　0.05).　The　EE　recognition　mode　was　significantly　more　sensitive　in　detecting　both　enamel　and　dentin　caries　than　human　eyes　(p　all　＜　0.05).　The　CNN　trained　with　the　EE　strategy,　the　best　performer　in　the　present　study,　showed　potential　utility　in　detecting　proximal　caries　on　periapical　radiographs　when　using　small　datasets.