Background:　Accurate　identification　of　anal　fistulas　is　essential,　as　it　directly　impacts　the　severity　of　subsequent　perianal　infections,　prognostic　indicators,　and　overall　treatment　outcomes.　Traditional　manual　recognition　methods　are　inefficient.　In　response,　computer　vision　methods　have　been　adopted　to　improve　efficiency.　Convolutional　neural　networks(CNNs)　are　the　main　basis　for　detecting　anal　fistulas　in　current　computer　vision　techniques.　However,　these　methods　often　struggle　to　capture　long-range　dependencies　effectively,　which　results　in　inadequate　handling　of　images　of　anal　fistulas.　Methods:　This　study　proposes　a　new　fusion　model,　CVT-HNet,　that　integrates　MobileNet　with　vision　transformer　technology.　This　design　utilizes　CNNs　to　extract　local　features　and　Transformers　to　capture　long-range　dependencies.　In　addition,　the　MobileNetV2　with　Coordinate　Attention　mechanism　and　encoder　modules　are　optimized　to　improve　the　precision　of　detecting　anal　fistulas.　Results:　Comparative　experimental　results　show　that　CVT-HNet　achieves　an　accuracy　of　80.66%　with　significant　robustness.　It　surpasses　both　pure　Transformer　architecture　models　and　other　fusion　networks.　Internal　validation　results　demonstrate　the　reliability　and　consistency　of　CVT-HNet.　External　validation　demonstrates　that　our　model　exhibits　commendable　transportability　and　generalizability.　In　visualization　analysis,　CVT-HNet　exhibits　a　more　concentrated　focus　on　the　region　of　interest　in　images　of　anal　fistulas.　Furthermore,　the　contribution　of　each　CVT-HNet　component　module　is　evaluated　by　ablation　experiments.　Conclusion:　The　experimental　results　highlight　the　superior　performance　and　practicality　of　CVT-HNet　in　detecting　anal　fistulas.　By　combining　local　and　global　information,　CVT-HNet　demonstrates　strong　performance.　The　model　not　only　achieves　high　accuracy　and　robustness　but　also　exhibits　strong　generalizability.　This　makes　it　suitable　for　real-world　applications　where　variability　in　data　is　common.These　findings　emphasize　its　effectiveness　in　clinical　contexts.　©　The　Author(s)　2025.