Fast　and　accurate　detection　of　infectious　bacteria　in　wounds　is　crucial　for　effective　clinical　treatment.　However,　traditional　methods　take　over　24　h　to　yield　results,　which　is　inadequate　for　urgent　clinical　needs.　Here,　we　introduce　a　deep　learning-driven　framework　that　detects　and　classifies　four　bacteria　commonly　found　in　wounds:　Acinetobacter　baumannii　(AB),　Escherichia　coli　(EC),　Pseudomonas　aeruginosa　(PA),　and　Staphylococcus　aureus　(SA).　This　framework　leverages　the　pretrained　ResNet50　deep　learning　architecture,　trained　on　manually　collected　periodic　bacterial　colony-growth　images　from　high-content　imaging.　In　in　vitro　samples,　our　method　achieves　a　detection　rate　of　over　95%　for　early　colonies　cultured　for　8　h,　reducing　detection　time　by　more　than　12　h　compared　to　traditional　Environmental　Protection　Agency　(EPA)-approved　methods.　For　colony　classification,　it　identifies　AB,　EC,　PA,　and　SA　colonies　with　accuracies　of　96%,　97%,　96%,　and　98%,　respectively.　For　mixed　bacterial　samples,　it　identifies　colonies　with　95%　accuracy　and　classifies　them　with　93%　precision.　In　mouse　wound　samples,　the　method　identifies　over　90%　of　developing　bacterial　colonies　and　classifies　colony　types　with　an　average　accuracy　of　over　94%.　These　results　highlight　the　framework＇s　potential　for　improving　the　clinical　treatment　of　wound　infections.　Besides,　the　framework　provides　the　detection　results　with　key　feature　visualization,　which　enhance　the　prediction　credibility　for　users.　To　summarize,　the　proposed　framework　enables　high-throughput　identification,　significantly　reducing　detection　time　and　providing　a　cost-effective　tool　for　early　bacterial　detection.