Bladder cancer is among the most common malignancies, with early detection being critical for effective treatment. This work investigates AI-based lesion detection in cystoscopic images, leveraging both YOLO and visual transformer (VT) architectures. Multiple datasets, including newly collected and publicly available sources, were systematically combined to train and evaluate detection models. Results show that increasing diversity and volume of training data significantly improves detection performance. Pretraining with colonoscopic images further improved model accuracy, indicating similarities in the appearance of lesions across different organs. While VT initially performed better, advanced YOLO outperformed VT with enriched data. These findings highlight the importance of heterogeneous datasets and model selection to advance automated bladder cancer detection.