Bidirectional Transformer with Sparse Attention for Gastrointestinal Disease Recognition

Gastrointestinal (GI) disease recognition is significant for early detection and effective treatment, leading to improved patient outcomes. In recent years, artificial intelligence (AI) techniques, particularly vision transformer, demonstrate their effectiveness in medical diagnosis. This paper adopts a novel approach, namely Bidirectional Transformer with Sparse Attention (BTSA) for GI disease recognition. The proposed model leverages the power of transformer architectures and incorporates sparse attention mechanisms to handle the diversity and complexity of GI diseases. By learning from large-scale datasets, BTSA can effectively identify patterns and anomalies associated with different GI diseases. The sparse attention mechanism boosts the representation learning on relevant regions and features, improving both performance and computational efficiency. Extensive experiments prove the outstanding performance of BTSA in GI disease recognition, surpassing existing models successfully. The proposed BTSA model holds significant potential to revolutionize the diagnosis and management of GI diseases, assisting healthcare professionals in making informed decisions and ultimately improving patient care. Further research and validation are required to explore the full capabilities and clinical utility of BTSA in real-world healthcare settings.