Colorectal cancer image recognition algorithm based on improved transformer

Aiming 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.