Image Segmentation of Intestinal Polyps using Attention Mechanism based on Convolutional Neural Network

The intestinal polyp is one of the common intestinal diseases, which is characterized by protruding lining tissue of the colon or rectum. Considering that they may become cancerous, they should be removed by surgery as soon as possible. In the past, it took a lot of manpower and time to identify and diagnose intestinal polyps, which greatly affected the treatment efficiency of medical staff. Because the polyp part looks similar to the normal structure of the human body, the probability of human eye misjudgment is high. Therefore, it is necessary to use advanced computer technology to segment the intestinal polyp image. In the model established in this paper, an image segmentation method based on convolution neural network is proposed. The Har-DNet backbone network is used as the encoder in the model, and its feature processing results are converted into three feature images of different sizes, which are input to the decoding module. In the decoding process, each output first expands the receptive field module and then fuses the feature image processed by the attention mechanism. The fusion results are input to the density aggregation module for processing to improve the operation efficiency and accuracy of the model. The experimental results show that compared with the previous Pra-Net model and Har-DNet MSEG model, the accuracy and precision of this method are greatly improved, and can be applied to the actual medical image recognition process, thus improving the treatment efficiency of patients.

@article{Zheng2023,
  title     = {Image Segmentation of Intestinal Polyps using Attention Mechanism based on Convolutional Neural Network},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {14},
  number    = {1},
  year      = {2023},
  publisher = {The Science and Information Organization},
  author    = {Xinyi Zheng and Wanru Gong and Ruijia Yang and Guoyu Zuo},
  doi       = {10.14569/IJACSA.2023.0140164},
  url       = {https://doi.org/10.14569/IJACSA.2023.0140164}
}