Aims: For the PhysioNet Challenge 2025, our team "bug busters" developed an approach to detect Chagas disease from electrocardiograms. This parasitic infection can be life-threatening when untreated, and ECG-based screening could direct limited resources more efficiently. Methods: We implemented a novel multi-stage cas- cading approach using five deep learning models: two ResNet18 variants with attention mechanisms (SE and CBAM), two SimpleCNN models, and an AttentionCNN. Our key innovation is a progressive filtering pipeline that ranks healthy samples by their prediction scores and re- moves those most confidently classified as healthy, creating increasingly focused training sets. Results: Our approach scored 0.369 in the official stage. Our internal testing yielded improved performance and the best model reached an average Challenge score of 0.495 with 5-fold cross-validation. Conclusion: The cascading multi-stage methodology shows promise for Chagas disease detection, overcoming the limitations of single-model approaches. Future work should investigate performance across diverse patient pop- ulations and explore interpretability of model decisions.