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.

Keywords:

• Clinical small-bowel images
• convolution neural network
• Joint-Net
• polyp recognition

Acknowledgments

The study protocol was approved by the Institutional Review Board and Ethics Committee of Changhai Hospital, Naval Medical University (Second Military Medical University) (IRB no. CHEC2017-138). The data for the study were collected between 2017 and 2020 and marked by experienced physicians.

Declaration of interest

The authors report no conflicts of interest. The authors alone are responsible for the content and writing of this article.

Correction Statement

This article has been republished with minor changes. These changes do not impact the academic content of the article.

Additional information

Funding

This work was supported by Natural Science Foundation of Shanghai [Grant Number 20ZR1437700] and the National Natural Science Foundation of China [Grant Number 61001164].