A novel Joint-Net model for recognizing small-bowel polyp images

Abstract

Introduction To automatically recognize polyps of enteroscopy images and avoid pathological change, a novel Joint-Net has been proposed. Material and methods The left half of the Joint-Net is constructed by transfer learning VGG16 and its right half is deepened based on the U-Net. In the previous two skip connections, a 3 × 3 convolution layer is added and the original two convolutions are replaced by the identity blocks. To connect the left and the right half part, the asymmetric convolution layer is used. In the output, the loophole-like structure is used. Results The enteroscopy images were obtained in Changhai Hospital of Shanghai. The mean values of Dice and intersection over union were 90.05% and 82.71%. The classification accuracy of normal images and polyp images was 93.50%. Conclusions The experiments show that the Joint-Net can segment and recognize the polyps successfully.