文献类型：期刊文献

英文题名：A Novel Neural Network Based on Transformer for Polyp Image Segmentation

作者：Wang, Kunyu[1]; Qian, Zhiqin[1]; Zhang, Wenjun[2]; Zhang, Mingda[2]; Luo, Qi[2]

机构：[1] East China University of Science and Technology, Department of Mechanical Engineering, Shanghai, 200237, China; [2] University of Saskatchewan, Department of Mechanical Engineering, Saskatoon, SK, S7N 5A9, Canada

年份：2023

起止页码：413

外文期刊名：2023 IEEE 3rd International Conference on Electronic Technology, Communication and Information, ICETCI 2023

收录：EI(收录号：20233214488941)

语种：英文

外文关键词：Computer architecture - Convolution - Convolutional neural networks - Deep learning - Diagnosis - Medical imaging

摘要：The segmentation of medical images is crucial in clinical medicine for diagnosis and treatment. Precise segmentation of polyp images is of significant importance in colonoscopy to decrease the missed detection rate and lower the risk of polyps progressing into colon cancer. Although Convolutional Neural Networks have provided an automated solution for segmentation and feature extraction, the disadvantages of this approach are lengthy training times and loss of local details. Conversely, Transformer excel in capturing global features and parallel processing, but require some artifacts. This paper introduces a novel image processing architecture that combines CNN and Transformers in a unique manner. The proposed model is evaluated on five polyp segmentation datasets, and the experimental results demonstrate that it achieves faster reasoning speed and higher accuracy compared to conventional CNN. ? 2023 IEEE.