Serous cavity effusion smears are widely used in cytological diagnosis, but manual examination can be labor-intensive and variable in accuracy. This study builds a vision transformer-based framework for identifying malignant cells from smear images collected from 161 patients and thousands of annotated patches. The model improves automated screening performance and offers a more precise computational tool for assisting cytological assessment in routine clinical workflows.