Colorectal cancer image recognition algorithm based on improved transformer

Author:

Qin Zhuanping,Sun Wenhao,Guo Tinghang,Lu Guangda

Abstract

AbstractAiming at the problems of the complex background of colorectal cancer tissue cell images and the difficulty of detection caused by the low differentiation of cancer cell regions, a deep learning method is used to detect the cancer cell regions. By integrating the skip feedback connection structure into U-Net and combining it with the Swin Transformer for feature extraction, we improve the multi-level feature extraction capabilities of the model. This algorithm enables end-to-end recognition of colorectal adenocarcinoma tissue images and achieves an accuracy of 95.8% on the NCT-CRC-HE-100K dataset, demonstrating its potential to significantly support colorectal cancer detection and treatment.

Funder

Tianjin Municipal Education Commission Scientific Research Program Project

Tianjin Science and Technology Plan Project of the Open Bidding for Selecting the Best Candidates

Publisher

Springer Science and Business Media LLC

Reference26 articles.

1. Mármol I, Sánchez-de-Diego C, Pradilla Dieste A, et al. Colorectal carcinoma: a general overview and future perspectives in colorectal cancer. Int J Mol Sci. 2017;18(1):197.

2. Xiusen Q, Wentai G, Wuteng C, et al. Advances in study of colorectal mucinous adenocarcinoma. Chin J Bases Clin Gen Surg. 2020;27(7):906–11.

3. Pei Xiaoyue Hu, Ling BL, et al. Clinicopathological and immunohistochemical features in different histological types of colorectal carcinoma. J Clin Pathol Res. 2020;40(8):1941–8.

4. Liu R, et al. AIMIC: deep learning for microscopic image classification. Comput Methods Programs Biomed. 2022;226: 107162.

5. Jiabao Z, Zhiyong X. Gland and colonoscopy segmentation method combining self-attention and convolutional neural network. Laser Optoelectron Progr. 2023;60(02):291–9.

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3