To address the fission detection problem of MIDOG 2025 Track 1, we propose a two-stage framework that takes into account the complex and heterogeneous environment and the possibility of artifacts. In the first stage, we use the enhanced YOLO11x with integrated EMA attention and LSConv to generate fission candidates and apply a low confidence threshold to increase recall. In the second stage, we use the ConvNeXt-Tiny classifier to filter out false positives and ensure accuracy. On the integrated dataset consisting of MIDOG++, MITOS_WSI_CCMCT, and MITOS_WSI_CMC, we achieve an F1 score of 0.882, which is 0.035 higher than the YOLO11x single-stage baseline. This performance is attributed to the improved precision (from 0.762 to 0.839), and we achieved an F1 score of 0.7587 on the MIDOG 2025 Track 1 preliminary test set.