  Bladder cancer ranks within the top 10 most diagnosed cancers worldwide and
is among the most expensive cancers to treat due to the high recurrence rates
which require lifetime follow-ups. The primary tool for diagnosis is
cystoscopy, which heavily relies on doctors' expertise and interpretation.
Therefore, annually, numerous cases are either undiagnosed or misdiagnosed and
treated as urinary infections. To address this, we suggest a deep learning
approach for bladder cancer detection and segmentation which combines CNNs with
a lightweight positional-encoding-free transformer and dual attention gates
that fuse self and spatial attention for feature enhancement. The architecture
suggested in this paper is efficient making it suitable for medical scenarios
that require real time inference. Experiments have proven that this model
addresses the critical need for a balance between computational efficiency and
diagnostic accuracy in cystoscopic imaging as despite its small size it rivals
large models in performance.
