Channel separation-based network for the automatic anatomical site recognition using endoscopic images

Gastroscopy is the preferred method to detect upper gastrointestinal lesions and has been widely adopted. For the diagnosis of gastrointestinal diseases, the first crucial step is to properly recognize the anatomical location. Image recognition using deep learning algorithms has made remarkable progress in the medical fields, and been increasingly used in gastrointestinal endoscopy. However, due to the similarity of many parts of the gastrointestinal tract, the accuracy of multi-site recognition based on traditional convolution neural network still needs to be improved. The effectiveness of convolutional neural networks (CNNs) to recognize anatomical sites in endoscopic images is explored and a channel-separation strategy for classification based on richer convolutional features is proposed. We use 1 x 1 conv. layer with channel depth 12 to extract and separate the depth feature information of anatomical sites and use global average pooling to output the final results. Compared with several classical networks, the proposed method is feasible and can improve the performance of anatomical site recognition of endoscopic images. The proposed method achieved 98.84% accuracy, 92.86% precision and 92.43% F1 score. The experimental results show that the proposed method has the potential to be applied to the automatic recognition of anatomical sites for clinical gastroscopy.