Adasan: Adaptive Cosine Similarity Self-Attention Network For Gastrointestinal Endoscopy Image Classification

Wireless capsule endoscopy plays an important role in the examination of gastrointestinal diseases. However, the large number of medical images produced by endoscopy makes it a time-consuming and labor-intensive work for doctors to examine. Clinically, the detection rate of small ulcers and superficial lesions is low. If these minor lesions are not screened and treated timely, they are likely to develop into cancer. Therefore, it is of great significance to develop computer-aided diagnostic algorithms to help doctors perform gastrointestinal image analysis. In this paper, we propose an adaptive cosine similarity network with self-attention module — AdaSAN, for automatic classification of gastrointestinal wireless capsule endoscope images. The experimental results on the clinical gastrointestinal image analysis dataset illustrate that our proposed method outperforms the state-of-the-art algorithms in the classification of inflammatory lesions, vascular lesions, polyps and normal images, with an average accuracy rate of 95.7%.