The　estimation　of　the　average　treatment　effect　on　the　treated　(ATT)　plays　an　essential　role　when　the　effect　of　an　intervention　or　a　treatment　on　those　participants　who　actually　received　it　is　the　focus.　However,　the　validity　of　conventional　estimation　methods　relies　on　the　precise　measurement　of　variables.　Misclassified　category　outcome　variables　may　cause　non-negligible　bias　when　estimating　ATT.　In　this　paper,　under　the　assumption　that　the　misclassification　probability　is　homogeneous,　we　develop　a　bias-corrected　estimation　method　to　consistently　estimate　ATT　when　internal　validation　data　are　available　for　a　subgroup　of　the　study　population.　We　further　derive　a　doubly　robust　estimator　by　augmenting　the　bias-corrected　estimator　to　provide　protection　against　treatment　model　misspecification.　Through　simulation　experiments　and　real　data　analysis,　we　demonstrate　the　satisfactory　performance　of　the　proposed　estimators.